Three Chinese labs shipped coding-focused models inside one week, and developers now face a price spread of 13x on input tokens. DeepSeek pushed its V4.1-Flash-1017 update on October 17, MiniMax made M3.1 Flash generally available on October 19, and Moonshot AI released Kimi K3.1 Pro on October 23. All three claim strong coding and agent results. Only one of them charges $2.00 per million input tokens.
This comparison puts DeepSeek V4.1 Flash against Kimi K3.1 Pro and MiniMax M3.1 Flash on specs, list prices, independent benchmarks, vendor claims, speed, open-weight status and migration cost. We use the newest build of each line as of October 26, 2026. That means DeepSeek-V4.1-Flash-1017, Kimi K3.1 Pro (plus its Highspeed variant) and MiniMax-M3.1-Flash. Older models appear only as predecessors.
A caution before the numbers. These models are days old, so the independent evidence is thin and some of it comes from vendors grading their own work. We label every score by who produced it, and we flag anything a single outlet reported. Where the sources disagree, we use the more conservative figure.
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DeepSeek V4.1 Flash vs Kimi K3.1 Pro vs MiniMax M3.1 Flash: Quick Verdict
If you only read one section, read this one. Kimi K3.1 Pro is the strongest model of the three on independent testing, and it costs the most by a wide margin. DeepSeek V4.1 Flash gives up ten index points but runs far cheaper per task and generates tokens roughly twice as fast as MiniMax. MiniMax M3.1 Flash is the cheapest per task on paper, but it carries the loudest accuracy warning and the weakest long-horizon terminal score.
| Job to be done | Best pick | Why |
|---|---|---|
| Hard, long-running coding agents | Kimi K3.1 Pro | Artificial Analysis Intelligence Index of 55, fourth of 215 models, and a 67% Terminal-Bench 4.0 result |
| High-volume coding at low cost | DeepSeek V4.1 Flash | Index of 45 at about $0.26 per task, plus the fastest measured output speed |
| Structured business automation | MiniMax M3.1 Flash | Vendor-reported Automation-Bench lead and the lowest cost per task, with caveats on accuracy |
| Lowest list price per token | Tie: DeepSeek (off-peak) and MiniMax | Both list $0.15 input and $0.60 output per million tokens |
| Open weights you can download today | MiniMax M3.1 Flash | Weights reportedly published October 23, while Kimi’s are planned for November 4 |
The honest answer for most teams is a split deployment. Route hard tasks to Kimi, send bulk work to DeepSeek or MiniMax, and measure success rates on your own repositories before you commit. The rest of this article shows the data behind that advice.
Release Timeline: Three Launches in Seven Days
The timing explains why this comparison is happening now. DeepSeek released the original V4.1-Flash on September 10, 2026, describing it as the smallest model in a new architecture family with native multimodal understanding. Five weeks later, on October 17, it replaced the launch build behind the same API alias with the 1017 checkpoint. Prices stayed put. SiliconANGLE reported that DeepSeek called the change a minor update, aimed at shorter task execution and shorter outputs.
MiniMax followed on October 19. Its M3.1 Flash reached general availability through the MiniMax API, the M Plan subscription and MiniMax Code. The release closed a stealth chapter. An anonymous model nicknamed Space Bunny had appeared on OpenRouter and OpenCode from September 23, and MiniMax confirmed it was an early build of M3.1 Flash. Our earlier coverage of the Space Bunny reveal covers that run in detail. Caixin Global reported that MiniMax used the release to cut its API prices.
Moonshot AI closed the week on October 23 with Kimi K3.1 Pro, a post-trained successor to Kimi K3, which launched in July 2026. It arrived with a Highspeed variant that Moonshot says runs two to three times faster at the same intelligence level. Moonshot also priced it a third below K3.
| Date (2026) | Event | Model |
|---|---|---|
| September 10 | Original V4.1-Flash released with native multimodal input | DeepSeek |
| September 14 | deepseek-v4-pro requests rerouted to V4.1-Flash at Flash rates until V4.1-Pro launches | DeepSeek |
| September 23 | Anonymous “Space Bunny” model appears on OpenRouter and OpenCode | MiniMax (early build) |
| October 15 | Nearly five-hour DeepSeek API outage, per SiliconANGLE | DeepSeek |
| October 17 | V4.1-Flash-1017 goes live on web, app and API | DeepSeek |
| October 19 | M3.1 Flash reaches general availability | MiniMax |
| October 23 | Kimi K3.1 Pro and Highspeed released, MiniMax weights published | Moonshot, MiniMax |
| November 4 (planned) | Kimi K3.1 Pro weights due under an open license | Moonshot |
We also covered the DeepSeek 1017 update and the MiniMax launch when they landed. The October 15 outage matters for the reliability discussion later, because DeepSeek shipped its update just two days after an incident it apologized for.
Specs Table: What Each Model Actually Offers
The table below uses only details that vendor documentation or at least two fetched sources confirm. Where a field is blank in public documentation, we say so rather than fill it with a guess, and the table shows Not listed.
| Spec | DeepSeek V4.1 Flash | Kimi K3.1 Pro | MiniMax M3.1 Flash |
|---|---|---|---|
| Developer | DeepSeek | Moonshot AI | MiniMax |
| Current build | V4.1-Flash-1017 | K3.1 Pro (plus Highspeed) | M3.1 Flash |
| Release date | Oct. 17 update (original Sept. 10) | Oct. 23 | Oct. 19 |
| API model ID | deepseek-flash | kimi-k3.1-pro, kimi-k3.1-pro-highspeed | MiniMax-M3.1-Flash |
| Context window | Not confirmed in docs we reviewed | 1,048,576 tokens | 1M tokens |
| Scale | Not stated in sources reviewed | Roughly 3T parameters (reported) | 428B total, about 23B active MoE (reported) |
| Input types | Native multimodal (V4.1 line) | Native visual understanding | Text, image, video |
| Output | Text | Text | Text only |
| Reasoning control | Not documented in sources reviewed | Always on, low/high/max, default max | Effort setting, thinking cannot be disabled |
| Open weights | Reportedly MIT-licensed | Planned Nov. 4, not yet released | Reportedly on Hugging Face since Oct. 23 |
| Consumer surfaces | DeepSeek web and app, Harness | Kimi web, Kimi Work, Kimi Code | M Plan, MiniMax Code |
| Plan tiers | No official plan table found | Free, Go, Plus, Pro, Max, Ultra | M Plan: Go, Explore, Build |
| Agent extras | Search and Fetch APIs at $0.75 per 1,000 requests | Highspeed variant, 2 to 3x faster | Anthropic SDK and OpenAI SDK support |
| Artificial Analysis Intelligence Index | 45 | 55 | 42 to 43 |
Three details deserve attention. First, Kimi’s reasoning is permanently on, and its default effort is the maximum setting. Second, MiniMax’s thinking cannot be switched off either, and one tracker reports that a request disabling it returns an HTTP 400 error. Third, DeepSeek keeps the stable alias deepseek-flash, so a pinned integration silently receives the 1017 checkpoint without any code change.
Always-on reasoning has a cost consequence that list prices hide. Every request pays for thinking tokens, and a default of maximum effort inflates both latency and spend. We return to that in the cost-per-task section.
API Pricing Compared: A 13x Spread on Input Tokens
List prices are where the three models separate most sharply. Kimi’s pricing page lists K3.1 Pro at $2.00 per million input tokens, $10.00 per million output tokens and $0.20 per million cached input tokens. DeepSeek and MiniMax both list $0.15 input and $0.60 output. Divide $2.00 by $0.15 and you get 13.3x on input. Divide $10.00 by $0.60 and the output gap widens to 16.7x.
| Model and tier | Input (per 1M) | Output (per 1M) | Cached input (per 1M) | Notes |
|---|---|---|---|---|
| DeepSeek V4.1 Flash, off-peak | $0.15 | $0.60 | Lower than standard input | Off-peak rates are 50% of peak |
| DeepSeek V4.1 Flash, peak | $0.30 | $1.20 | Lower than standard input | Peak runs twice the off-peak rate |
| MiniMax M3.1 Flash | $0.15 | $0.60 | $0.015 | Flat pricing, no peak tier |
| Kimi K3.1 Pro | $2.00 | $10.00 | $0.20 | Flat across the context window |
| Kimi K3.1 Pro Highspeed | $4.00 | $20.00 | $0.20 | Moonshot claims 2 to 3x speed |
| Kimi K3 (predecessor) | $3.00 | $15.00 | $0.30 | Launched July 2026 |
DeepSeek’s peak and off-peak structure complicates budgeting. One tracker reports that peak pricing applies on weekdays from 09:00 to 12:00 and 14:00 to 18:00 Beijing time, with weekends and Chinese public holidays billed off-peak. Treat that schedule as reported rather than official, and confirm it in DeepSeek’s billing documentation before you plan around it. MiniMax avoids the question with one flat rate.
Cache pricing changes the picture for agents, which resend the same long prefix on every step. MiniMax lists cached input at $0.015, a 90% discount. VentureBeat’s rate table reportedly puts DeepSeek’s cache hits at $0.003 off-peak and $0.006 at peak. Kimi’s $0.20 is the highest of the three, and Kimi also charges for cache writes at $2.00 per million tokens for a five-minute TTL or $4.00 for a one-hour TTL.
What a Real Monthly Bill Looks Like
We ran the list prices through two workloads. The first is a chat-style coding assistant with 10 million input tokens and 2 million output tokens a month. The second is an agent loop that sends 100 million input tokens, serves 80% of them from cache, and produces 10 million output tokens. This is arithmetic on list prices. Real bills also depend on reasoning tokens, cache-write charges and taxes.
| Workload | DeepSeek (off-peak) | DeepSeek (peak) | MiniMax | Kimi K3.1 Pro | Kimi Highspeed |
|---|---|---|---|---|---|
| Chat assistant: 10M in, 2M out | $2.70 | $5.40 | $2.70 | $40.00 | $80.00 |
| Agent loop: 100M in (80% cached), 10M out | $9.24 | $18.48 | Not listed | $156.00 | Not modeled |
The agent-loop row uses DeepSeek’s reported cache rates. On that workload Kimi costs about 15x what MiniMax does and about 17x what off-peak DeepSeek does. The gap is large enough that Kimi only pays for itself if its higher success rate removes retries, human review time or failed runs. The per-task data later in this article tests exactly that.
For context on where Kimi’s price sits in the wider market, VentureBeat noted that its rates match Claude Sonnet 5.5 and GPT-6 Sol at $2.00 and $10.00, and run half of Claude Opus 5.5’s. The earlier Kimi K3 price comparison shows how much has changed since July. Chinese labs no longer compete purely on being the cheap option at the top end.
Benchmarks From Three Sources: Independent, Vendor and Third-Party Indexes
We group results by who ran them. Independent numbers come from Artificial Analysis, reported through VentureBeat, R&D World and other outlets. Vendor numbers come from DeepSeek, MiniMax and Moonshot. A third set comes from the Vals Index, relayed by a tracker. Percentages, index points and Elo ratings measure different things, so compare within a row rather than across them.
Independent Scores: The Artificial Analysis Intelligence Index
Artificial Analysis placed Kimi K3.1 Pro fourth on its Intelligence Index at launch, with a score of 55. R&D World put that fourth among 215 models, behind Claude Opus 5.5 at 58, Claude Sonnet 5.5 at 56 and Meta’s Muse Wonder Full at 56. Kimi’s predecessor K3 scored 44, so the post-training update added 11 points. VentureBeat reported that Kimi now sits above DeepSeek V4.1-Flash-1017 on the index.
The two budget models trail. A DeepSeek-focused news hub reported index scores of 45 for DeepSeek V4.1 Flash 1017 at maximum effort, up 6 points from the 39 of the September build, and 42 for MiniMax M3.1 Flash. Another tracker puts MiniMax at 42 to 43 and MiniMax’s earlier M3 at 29. We carry the range for MiniMax and note that the DeepSeek hub also sells coding plans, so it is not a fully neutral party.
| Metric (Artificial Analysis) | DeepSeek V4.1 Flash 1017 | Kimi K3.1 Pro | MiniMax M3.1 Flash |
|---|---|---|---|
| Intelligence Index | 45 | 55 | 42 to 43 |
| Cost per index task | About $0.26 | $2.11 | About $0.22 |
| Measured output speed | About 209 tokens/s | 77 tokens/s | About 105 tokens/s |
| Output tokens per task | About 89k | Not reported per task | About 79k |
| Humanity’s Last Exam | Not reported in sources | 52% | 48% |
| Cyber Index | 57% | 64% | Not reported in sources |
The speed row shows the biggest surprise. Kimi’s measured 77 tokens per second is the slowest of the three, which is why the Highspeed variant exists. MiniMax claims 200 or more tokens per second in everyday use, but Artificial Analysis measured about 105, according to one tracker. That gap matters because MiniMax’s “nearly three times faster” claim compares M3.1 Flash with MiniMax M3, not with rivals.
Vendor-Reported Coding and Agent Scores
Vendor scores come from different harnesses and conditions, so they are not a controlled race. They still show where each lab thinks its model is strongest. DeepSeek said its 1017 build resolved 77% of tasks on DeepSWE v1.1 in its own testing, up from 74.2% for the September version. MiniMax reported 73.7% on the same benchmark and 62.2% on Automation-Bench, against 60.5% for DeepSeek V4.1 Flash. Moonshot’s own sheet puts Kimi at 51.7 on SWE-Marathon versus 46.3 for Claude Opus 5.5.
| Benchmark | DeepSeek V4.1 Flash 1017 | Kimi K3.1 Pro | MiniMax M3.1 Flash | Who ran it |
|---|---|---|---|---|
| DeepSWE v1.1 | 77.0% | Not reported in sources | 73.7% | Each vendor, own harness |
| Automation-Bench | 60.5% | Not comparable (AA variant: 73%) | 62.2% | MiniMax |
| Terminal-Bench 4.0 | Not reported in sources | 67% (AA), 65.7 (Moonshot) | 25.25% | AA, Moonshot, independent eval index |
| SWE-Marathon | Not reported in sources | 51.7 | Not reported in sources | Moonshot |
| OSWorld | Not reported in sources | Not reported in sources | 61.7% | MiniMax |
| BankerToolBench | Not reported in sources | Not reported in sources | 76.2 | MiniMax |
Read the Terminal-Bench row carefully. Artificial Analysis measured Kimi at 67%, a jump of 54 points over Kimi K3’s 13%. Moonshot’s own run put it at 65.7, narrowly behind Opus 5.5 at 66.4. MiniMax’s 25.25% comes from an independent evaluation index and points to weakness on long-horizon terminal work. The Vals Index tracker also ranked MiniMax 15th of 23 at 53.78%, with an average latency of 43 minutes 18 seconds per test at maximum effort.
The MiniMax accuracy story has a second layer. Artificial Analysis, as relayed by VentureBeat, found that M3.1 Flash answered more than half of the factual questions correctly but showed an unusually high tendency to give a wrong answer instead of admitting uncertainty. We examined that finding in our piece on MiniMax M3.1 Flash accuracy doubts. For unattended agents, abstention behavior can matter more than a coding score.
Speed, Token Burn and Cost Per Task
List prices tell you what a token costs. Cost per task tells you what a finished job costs, and the two can point in opposite directions. Reasoning models burn tokens on thinking, and a model that thinks longer can erase a price advantage. Artificial Analysis publishes a weighted cost per index task that folds input, cache and output charges together, which makes it the fairest number we have for this trio.
Kimi K3.1 Pro came in at $2.11 per index task, according to R&D World. DeepSeek V4.1 Flash 1017 landed near $0.26 and MiniMax M3.1 Flash near $0.22, based on figures a DeepSeek-focused hub reported from Artificial Analysis charts. Kimi therefore costs roughly 8x DeepSeek and roughly 10x MiniMax per task. The list-price gap was 13x on input and 17x on output, so Kimi’s higher intelligence closes only a sliver of the cost difference.
| Model | Index score | Cost per task | Index points per task dollar (our math) |
|---|---|---|---|
| MiniMax M3.1 Flash | 42 | $0.22 | Not listed |
| DeepSeek V4.1 Flash 1017 | 45 | $0.26 | Not listed |
| Kimi K3.1 Pro | 55 | $2.11 | About 26 |
That last column is simple division, and it flatters the cheap models because intelligence does not scale linearly with score. Ten extra index points near the top of the scale represent harder problems solved. Still, if your tasks are routine, paying seven times more per index point makes little sense. If your tasks are the hard 10% that cheaper models fail, Kimi’s premium can be the cheaper option once you count retries.
Token Usage and the Thinking Tax
Token appetite differs sharply. Artificial Analysis counted about 150 million output tokens for Kimi K3.1 Pro to complete its index, against a median of 87 million for the models it tracks. For MiniMax, a tracker relaying Artificial Analysis data reported about 180 million against a median of 100 million and called the model very verbose. Medians shift as models are added, so read each as a within-source comparison.
Per task, the DeepSeek hub’s charts show about 89k output tokens for DeepSeek 1017 and about 79k for MiniMax, including reasoning. DeepSeek’s 1017 build used roughly 59k reasoning tokens, a few thousand fewer than the September build, while scoring higher. That fits DeepSeek’s stated goal of shorter outputs. Both Kimi and MiniMax default to maximum effort, so teams that never set the parameter pay for the longest possible thinking on every call.
Our related comparison of MiniMax M3.1, GPT-6 Luna and DeepSeek V4.1 Flash looks at how the same two budget models stack up against an OpenAI option. The pattern repeats there. Price per token is the headline, and tokens consumed per task decides the bill.
One social-media post claimed that DeepSeek V4.1 Flash matched or beat Kimi K3.1 Pro on a high-effort test at $0.49 versus $24.12. We treat that as an anecdote. It is a single user’s run with no published method, and Artificial Analysis data shows Kimi ahead on its aggregate index. The cost ratio in the post does point the same direction as the independent per-task figures, though.
Coding Performance: Where Each Model Wins and Loses
Coding is the use case all three vendors lead with, so it deserves a model-by-model read. The honest picture is that each model owns a different slice of the work. None of them sweeps every benchmark, and the vendor sheets confirm it.
Kimi K3.1 Pro shows its biggest gains in sustained work. VentureBeat’s summary of the Artificial Analysis data lists Terminal-Bench 4.0 rising from 13% to 67%, AutomationBench-AA from 58% to 73%, and the GDPval-AA Elo rating from 1,534 to 1,846. Gains on pure reasoning were smaller. Humanity’s Last Exam rose from 47% to 52% and SciCode from 59% to 61%. The pattern points to better task execution rather than an across-the-board leap, which suits repository maintenance and debugging loops that alternate between reading files, editing and running tests.
Kimi has a clear weak spot. On Terminal-Bench Science 0.1, Moonshot’s own comparison has K3.1 Pro at 38.1 against 63.3 for Claude Opus 5.5 and 68.1 for GPT-6 Astra. R&D World also reported that an Arena evaluator who tested 3D scenes, animations and websites rated Kimi’s output above GPT-5.6 Sol and below GPT-6.1 Sol, and judged it not quite at the frontier. Scientific-computing teams should keep an alternative in the loop.
DeepSeek V4.1 Flash 1017 is the efficiency play. Its 77.0% on DeepSWE v1.1 is the highest vendor-reported software-engineering score among the three, though DeepSeek ran it in its own harness. SiliconANGLE noted that Claude Opus 5 sat at 74% in the same table and that Anthropic’s model still took seven of the eleven benchmarks DeepSeek published. The 1017 update targeted agent frameworks directly, including DeepSeek Harness, Tencent’s CodeBuddy and WorkBuddy.
MiniMax M3.1 Flash has the most uneven record. MiniMax reports 73.7% on DeepSWE and 62.2% on Automation-Bench, and an independent index gave it 73.94% on Vibe Code Bench v1.1, up from 47.57% for M3. Yet the same index shows 25.25% on Terminal-Bench 4.0 and 7.50% on HLAB-Vals-1.1, which the evaluator called weak on long-horizon agentic work. Some developers on Reddit accused the vendor of tuning for benchmarks, and complaints about context drift appeared in early threads. Most of those posts describe the Space Bunny early build, which MiniMax says differed from the final model.
Agent Workloads: Tools, Harnesses and Long-Running Tasks
Agents stress different things than single-shot coding. They call tools, resend long histories, recover from errors and run for minutes or hours. Three attributes decide fit: cache economics, ecosystem integration and behavior under uncertainty.
On cache economics, MiniMax and DeepSeek win decisively. A prefix that costs $0.015 or less per million cached tokens makes long agent histories nearly free to resend. Kimi’s $0.20 cache-hit rate is still a 90% discount off its $2.00 input price, but it is more than 13 times MiniMax’s rate. Kimi also bills cache writes, which cheaper rivals do not appear to emphasize. An agent that rewrites its cache often will feel that.
On ecosystem, DeepSeek leans hardest into agent tooling. On October 17 it opened a Search API that returns up to 50 ranked web results and a Fetch API that converts a page to Markdown, each at $0.75 per 1,000 successful requests. Calls made through DeepSeek Harness, Claude Code or Codex stay free until October 31 at 16:00 UTC, and billing starts afterward. Moonshot counters with Kimi Work and Kimi Code. MiniMax offers MiniMax Code and supports the Anthropic and OpenAI SDKs, which lowers the cost of swapping it into existing tools.
On behavior under uncertainty, the evidence favors caution with MiniMax. A model that guesses rather than abstains can cause damage when it holds write access. One developer thread cited a Space Bunny run that deleted main project files, and while that was the early build, it shows why abstention tests belong in any pilot. Kimi’s own materials point the other way. Moonshot reports that on a stress set of conversations users had flagged for factual errors, the per-statement error rate fell from 23.35% to 6.85% versus K3. The company stresses that the sample deliberately targets error-prone cases, so it is not an everyday error rate.
Open Weights and Licensing: What You Can Self-Host
Open weights matter for privacy, cost control and sovereignty, and the three labs sit at different points. MiniMax moved first. Reports say it published M3.1 Flash weights on Hugging Face and ModelScope on October 23, one of them citing MXFP8 format under a MiniMax Community license. Commenters and one tracker describe a 428B-total, 23B-active mixture-of-experts design. MiniMax also announced that it completed China’s technology-export registration for overseas licensing of certain recent and upcoming models.
DeepSeek’s weights are reportedly MIT-licensed, and its inference stack is open too. The company released DeepInfer, an inference framework that powers V4.1 Flash in production, under Apache 2.0 on October 20. Our coverage of DeepInfer explains what that release gives self-hosters. Treat the MIT detail as reported until you read the license file in the repository you plan to download.
Kimi is the outlier. Moonshot calls K3.1 Pro open-weight, but the weights have not shipped. Reports say the company plans to publish them on November 4, 2026, and until then Artificial Analysis lists the model as proprietary. VentureBeat expects it to become the leading open-weights model on release. Do not build a self-hosting plan around a date that has not arrived. With roughly 3 trillion parameters reported, even a delivered checkpoint will demand a serious multi-GPU server.
| Question | DeepSeek V4.1 Flash | Kimi K3.1 Pro | MiniMax M3.1 Flash |
|---|---|---|---|
| Weights available on Oct. 26? | Reportedly yes | No | Reportedly yes |
| License | Reportedly MIT | Open license promised, terms unpublished | MiniMax Community license (reported) |
| Inference code | DeepInfer, Apache 2.0 | Not announced in sources | vLLM engineering partnership (reported) |
| Practical self-hosting scale | Not stated in sources | Roughly 3T parameters, cluster class | 428B total, 23B active (reported) |
Real-World Examples: What Each Model Has Already Done
Benchmarks only go so far. These documented examples show what the models do under live conditions, along with the source behind each claim.
- Space Bunny on OpenCode (MiniMax). The anonymous early build reportedly held the number-one spot on OpenCode for 12 consecutive days and served 106 trillion tokens by OpenCode’s own account. MiniMax cites more than 200 trillion tokens in total. The figures do not reconcile publicly, and the final model differs from the stealth build.
- Tencent CodeBuddy and WorkBuddy (DeepSeek). DeepSeek tuned the 1017 build for these two Tencent products, and SiliconANGLE reported that WorkBuddy has offered V4.1-Flash since the September 10 launch. It is a live deployment inside a desktop office agent.
- A Godot 3D action game (Kimi). Moonshot’s showcase describes K3.1 Pro building a 3D action-adventure in Godot 4.7.2 with five explorable regions, procedural terrain, third-person combat, a two-phase boss fight, a day-night cycle and campfire fast travel. The demo is vendor-selected, so treat it as a ceiling rather than an average.
- A walking simulator (Kimi). In Moonshot’s technical account, relayed by R&D World, K3.1 Pro built a simulator around an open-source anatomical model, found and corrected 36 errors in shoulder-muscle calculations, and retrained the controller until the simulated human managed alternating strides for 11.2 seconds.
- Financial and compliance work (MiniMax). MiniMax reports 76.2 on BankerToolBench, which covers multi-step planning, consistency across documents and chained tool calls, plus 82% on CyberGym for vulnerability discovery and validation. Both are vendor claims.
- Business operations simulation (Kimi). Moonshot says K3.1 Pro scored 37.0 on a long-horizon business-management benchmark covering cash, purchasing, inventory and pricing, about 4.2 times Kimi K3’s result.
- Agent search (DeepSeek). The new Search and Fetch APIs let a coding agent look up documentation and read pages for $0.75 per 1,000 calls, and calls routed through Harness, Claude Code or Codex carry no charge through October 31.
Two lessons stand out. Kimi’s examples are the most ambitious but also the most curated, while DeepSeek’s are the most practical because they run inside shipping products. MiniMax sits between them, with the largest real traffic volume but an early build behind it.
Which Model for Which Job: Six Use-Case Recommendations
We match each workload to the model whose strengths fit it. These are judgments built on the data above, not guarantees, so validate each against a sample of your own tasks.
| Use case | Recommended model | Reasoning | Watch out for |
|---|---|---|---|
| 1. Autonomous repo maintenance and debugging loops | Kimi K3.1 Pro | 67% Terminal-Bench 4.0 and the highest index score | 77 tokens/s speed, $2.11 per task |
| 2. High-volume code review, summaries and autocomplete | DeepSeek V4.1 Flash | Cheap, fastest measured speed, strong DeepSWE claim | Peak pricing and reported weekday windows |
| 3. Spreadsheet, finance and back-office automation | MiniMax M3.1 Flash | Lowest cost per task, Automation-Bench lead (vendor) | Add abstention tests before granting write access |
| 4. Screenshot and video understanding in dev tools | MiniMax or Kimi | MiniMax accepts video input and Kimi has native visual understanding | DeepSeek’s media limits were not documented in sources |
| 5. Security research and vulnerability triage | Kimi, with a second opinion | 64% Cyber Index, ahead of DeepSeek’s 57% | Moonshot added cyber-abuse restrictions at launch |
| 6. Self-hosted, data-sensitive deployments | MiniMax now, Kimi after Nov. 4 | MiniMax weights are reported live and Kimi’s are scheduled | Cluster-class hardware for either |
Procurement adds a seventh consideration. One tracker reported a U.S. Department of Homeland Security order that removes DeepSeek products from federal civilian agencies, and said MiniMax pitches a U.S.-hosted, zero-data-retention option through Ollama. We found each claim in a single source, so verify both with your compliance team. DeepSeek’s October 15 outage, which lasted nearly five hours, adds a reliability question for production use as well.
Migration Guide: Moving Workloads Between These Models
Switching models in an agent stack is rarely a one-line change. The three vendors differ in sampling rules, reasoning controls and retirement schedules. This checklist covers the traps that surfaced in the documentation we reviewed.
- Inventory your model IDs and retirement dates. MiniMax will stop serving M2.5 and M2.7 to API requests on November 18, 2026. Since October 19, M Plan and Token Plan traffic for those names already routes to M3.1 Flash, so outputs changed without any code edit. Moonshot has discontinued kimi-k2.5 (August 31), the kimi-k2 series (May 25) and kimi-latest (January 28). DeepSeek routes deepseek-v4-pro requests to V4.1-Flash at Flash rates until V4.1-Pro launches.
- Build a private evaluation set. Pull 50 to 200 real tasks from your repositories with known-good outcomes. Public benchmarks cannot tell you how a model handles your build system.
- Set reasoning effort explicitly. Kimi accepts low, high and max, defaulting to max. MiniMax uses a five-tier effort setting that also defaults to the maximum. Test lower tiers on simple tasks to cut latency and token spend.
- Respect Kimi’s fixed sampling. Moonshot’s documentation fixes temperature at 1.0, top_p at 0.95, n at 1 and both penalties at 0, and advises against passing them. Remove your own overrides.
- Restructure prompts for caching. Put stable instructions and repository context at the start and variable content at the end. Cache hits cut costs 90% or more on all three vendors.
- Route by difficulty. Send routine calls to the cheap tier and escalate failures. The sketch below shows the pattern.
- Add abstention tests. Include prompts where the right answer is that the model does not know, especially before you point MiniMax at write access.
- Plan for billing changes. DeepSeek’s Search and Fetch billing starts after October 31, and its peak rates run double. Kimi’s cache writes carry a charge.
- Keep a fallback. Wire a second provider behind the first so an outage like DeepSeek’s October 15 incident does not stall your pipeline.
The router below keeps vendor details in environment variables, so you can confirm each base URL and SDK behavior in the vendor’s own documentation. The step-by-step DeepSeek V4.1 Flash API setup guide covers key creation and first calls.
import os
from openai import OpenAI
TIERS = {
"cheap": {"model": "deepseek-flash", "base_url": os.environ["CHEAP_BASE_URL"], "key": os.environ["CHEAP_API_KEY"]},
"mid": {"model": "MiniMax-M3.1-Flash", "base_url": os.environ["MID_BASE_URL"], "key": os.environ["MID_API_KEY"]},
"hard": {"model": "kimi-k3.1-pro", "base_url": os.environ["HARD_BASE_URL"], "key": os.environ["HARD_API_KEY"]},
}
def run(task_prompt, tier="cheap", passes_check=lambda text: True):
order = ["cheap", "mid", "hard"]
for name in order[order.index(tier):]:
cfg = TIERS[name]
client = OpenAI(base_url=cfg["base_url"], api_key=cfg["key"])
reply = client.chat.completions.create(
model=cfg["model"],
messages=[{"role": "user", "content": task_prompt}],
)
text = reply.choices[0].message.content
if passes_check(text):
return name, text
return "hard", textSupply a real passes_check function, such as running unit tests against the generated patch. Without an automatic check, escalation never triggers and you pay for the cheap tier’s mistakes in review time.
Pros, Cons and Risks of Each Model
| Model | Pros | Cons |
|---|---|---|
| DeepSeek V4.1 Flash | Cheap at $0.15 and $0.60 off-peak. Fastest measured output at about 209 tokens/s. Highest vendor-reported DeepSWE score at 77.0%. Search and Fetch APIs for agents. Open inference stack. | Index 45 trails Kimi by 10 points. Peak pricing doubles rates. Nearly five-hour outage on Oct. 15. Context window not documented in sources we reviewed. Reported U.S. federal restrictions. |
| Kimi K3.1 Pro | Top independent score at 55 (fourth of 215). 67% Terminal-Bench 4.0. Strong sustained-agent gains over K3. Price cut a third from K3. 1,048,576-token context. | 13x to 17x the list price of rivals. Slowest measured speed at 77 tokens/s. Weights unreleased until a planned Nov. 4 date. Weak on Terminal-Bench Science. Mandatory top-up before API use, reportedly. |
| MiniMax M3.1 Flash | Lowest cost per task at about $0.22. Flat $0.15 and $0.60 pricing. Cheapest cache hits at $0.015. Video input. Weights reportedly available now. | High tendency to answer wrongly instead of abstaining. 25.25% Terminal-Bench 4.0. Thinking cannot be disabled. Vendor speed claim of 200+ tokens/s versus about 105 measured. |
Beyond the table, two risks cut across all three. First, benchmark credibility. VentureBeat’s reporting on the Kimi launch also mentioned allegations of paid influencers on X around the release, so social-media enthusiasm for any of these models deserves skepticism. Second, safety posture. Moonshot said it restricted the model’s hacking capability at launch, and the Artificial Analysis cyber results still show strong security performance. Teams in sensitive environments should run their own red-team checks.
Final Verdict: Pick by Task Difficulty, Not by Brand
Kimi K3.1 Pro wins on capability. Artificial Analysis ranks it fourth of 215 models, it posts the best Terminal-Bench result of the three, and its gains over K3 concentrate in the sustained agent work developers care about. It loses on price, speed and openness today. At $2.11 per index task it costs about 8x DeepSeek and about 10x MiniMax, and its weights have not shipped.
DeepSeek V4.1 Flash wins on balance. An index of 45 at about $0.26 per task and roughly 209 tokens per second makes it the safest default for volume coding work, and the 1017 update improved score and token efficiency without touching prices. Its risks are operational. Plan for outages and possible procurement limits.
MiniMax M3.1 Flash wins on cost per task and openness, and loses on trust. Until independent tests of the final checkpoint show better abstention and terminal performance, keep it on supervised, structured workloads. Our broader ranking of the best AI model for coding places these three alongside Western rivals, and the AI and machine learning hub tracks each follow-up as new data lands.
| Priority | Choose | Evidence |
|---|---|---|
| Highest quality | Kimi K3.1 Pro | Index 55, Terminal-Bench 4.0 at 67% |
| Best all-round default | DeepSeek V4.1 Flash | Index 45, about $0.26 per task, about 209 tokens/s |
| Lowest bill | MiniMax M3.1 Flash | About $0.22 per task, $0.015 cache hits |
| Self-hosting this week | MiniMax M3.1 Flash | Weights reported live since Oct. 23 |
Re-check this comparison on November 4. If Moonshot publishes the Kimi weights on schedule, the self-hosting row changes, and independent testing of MiniMax’s final checkpoint may shift the trust row.
Frequently Asked Questions
Is Kimi K3.1 Pro better than DeepSeek V4.1 Flash?
On independent testing, yes. Artificial Analysis gives Kimi K3.1 Pro an Intelligence Index of 55 against about 45 for DeepSeek V4.1 Flash 1017. DeepSeek is far cheaper per task, at roughly $0.26 versus $2.11, and about 2.7x faster in measured output speed.
Which of the three models is cheapest?
DeepSeek off-peak and MiniMax tie on list price at $0.15 input and $0.60 output per million tokens. MiniMax edges ahead on cost per Artificial Analysis task at about $0.22, and it lists cached input at $0.015 per million tokens.
Are the weights for these models open?
MiniMax’s weights were reportedly published on October 23 and DeepSeek’s are reportedly MIT-licensed. Moonshot plans to publish Kimi K3.1 Pro’s weights on November 4, 2026, so they are not available yet. Read each license before commercial use.
Can I turn off thinking on Kimi or MiniMax?
Not according to the documentation we reviewed. Kimi K3.1 Pro always reasons, with low, high and max effort levels and max as the default. MiniMax M3.1 Flash also requires thinking, and a tracker reports that disabling it returns an HTTP 400 error. Lower the effort setting to save tokens.
What happened to DeepSeek V4 Pro?
No retirement has been confirmed. The routing is temporary: from 04:00 UTC on September 14, 2026, requests to deepseek-v4-pro are routed to V4.1-Flash at V4.1-Flash rates until V4.1-Pro launches.
How reliable are the benchmark numbers?
Treat independent scores from Artificial Analysis and the Vals Index as the firmest evidence, and treat vendor scores as directional. Vendors use different harnesses and settings. All three models launched within days of this article, so more independent tests will arrive and may move the rankings.
Do I need to change code when MiniMax retires M2.5 and M2.7?
Yes, if you call those models through pay-as-you-go API access. They stop working for API requests on November 18, 2026. M Plan and Token Plan users already route to M3.1 Flash automatically, so re-run your regression tests because outputs have changed.
