Moonshot AI shipped Kimi K3.1 Pro in October 2026, and Bloomberg covered the debut. Soon after, the question on every developer forum was the same. Does a model that lists $10 per million output tokens really belong in the same ai models ranking as Claude Opus 5.5 and GPT-6 Astra, which lists $50?
The short answer is close, but not equal. Reported Terminal-Bench 4.0 scores put Kimi K3.1 Pro at 65.7%, Opus 5.5 at 66.4% and Astra at 58.2%. On the Artificial Analysis Intelligence Index, Opus 5.5 scores 58 and Kimi K3.1 Pro scores 55. We covered the launch itself in our Kimi K3.1 Pro debut report. This piece goes further and sets the three flagships side by side on specs, benchmarks, pricing, openness, safety posture and migration cost.
A word on method before the numbers. Every figure below comes from a current-facts sheet built on October 28, 2026 from vendor pages and recent reporting. Where a number appears in only one source, the text says so and hedges it. Where sources conflict, we use the conservative reading and flag the conflict. Benchmark claims in launch week are mostly vendor-reported or aggregator-reported, so treat every score as provisional until independent labs publish their own runs.
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Kimi K3.1 Pro vs Claude Opus 5.5 vs GPT-6 Astra: The Quick Answer
If you only read one section, read this one. Claude Opus 5.5 holds the top spot on both headline measures we could source, but only by a thin margin over Kimi K3.1 Pro. GPT-6 Astra trails on Terminal-Bench 4.0 and costs the most per token. Kimi K3.1 Pro is the price and openness story of the group, with open weights promised for November 4, 2026.
| Category | Leader | Evidence |
|---|---|---|
| Agentic coding score (Terminal-Bench 4.0) | Claude Opus 5.5 | 66.4% vs 65.7% (Kimi) vs 58.2% (Astra) |
| Overall index (Artificial Analysis) | Claude Opus 5.5 | 58 vs 55 for Kimi K3.1 Pro |
| Lowest list price per token | Kimi K3.1 Pro | $2 input and $10 output per million tokens |
| Openness | Kimi K3.1 Pro | Weights planned for Nov. 4, 2026 (not yet released) |
| Longest time on the market | GPT-6 Astra | Announced Sept. 3, 2026 |
| Efficiency gain over predecessor | Claude Opus 5.5 | Anthropic says it costs 40% less to run than Opus 5 on typical workloads |
That table hides some nuance, so the sections below unpack each row. The headline takeaway is simple. If your budget is fixed and your workload is token-heavy, Kimi K3.1 Pro deserves a serious trial. If you need the best score on agentic coding today and can pay for it, Opus 5.5 still wins. If you already live inside ChatGPT Pro, Astra is in your plan, and the cost question is mostly moot.
Specs Table: What Each Model Actually Offers
Spec sheets for frontier models are thinner than they look. Vendors publish context windows, prices and model identifiers, but rarely parameter counts or architecture details. The table below lists only what the vendors or multiple outlets have confirmed. Where a cell says not confirmed, our sources did not establish the figure, and we chose to leave it blank instead of guessing.
| Spec | Kimi K3.1 Pro | Claude Opus 5.5 | GPT-6 Astra |
|---|---|---|---|
| Developer | Moonshot AI | Anthropic | OpenAI |
| Release date | Oct. 23, 2026 | Sept. 22, 2026 | Announced Sept. 3, 2026 |
| Position in its family | Flagship, first in the K3.1 series | First model in the Claude 5.5 family | Listed in OpenAI’s Oct. 2 model guide as its most intelligent model for the best results |
| Context window | 1 million tokens (1,048,576 per the official docs) | Not confirmed | Not confirmed |
| Parameters | Roughly 3 trillion total | Not disclosed | Not disclosed |
| Weights | Planned open release Nov. 4, 2026 | No open release announced | No open release announced |
| Input price (per 1M tokens) | $2.00 | Reported $4 (single source) | $10.00 |
| Output price (per 1M tokens) | $10.00 | Reported $20 (single source) | $50.00 |
| Cached input price | $0.20 | Reported $0.20 (single source) | $1.00 |
| Neighboring models | K3.1 Pro Highspeed at $4 in and $20 out | Sonnet 5.5 and Haiku 5.5 sit below it | GPT-6.1 Sol and GPT-6.1 Terra sit alongside it |
| Reported Terminal-Bench 4.0 | 65.7% | 66.4% | 58.2% |
| Artificial Analysis index | 55 | 58 | Not confirmed |
| Access routes | Kimi website, mobile apps, Kimi Work, Kimi Code, Kimi API | Claude apps, Claude API, Claude Duet preview | ChatGPT Pro reasoning, ChatGPT Work, Codex, API |
| API identifier | kimi-k3.1-pro | See Anthropic docs | See OpenAI docs |
| Entry barrier | $1 minimum top-up unlocks the model | Anthropic account (plan or API) | Plus, Pro or API account |
Two details deserve attention. First, Moonshot’s official quickstart says K3.1 Pro unlocks after a successful top-up of at least $1, and that your cumulative top-ups set your account tier and rate limits (concurrency, requests per minute, tokens per minute and tokens per day). That is a low bar to entry, but it also means a brand-new account starts with tight limits. Second, Kimi’s roughly 3 trillion total parameters make it enormous for an open-weight release, which will matter when we reach the self-hosting question.
Reading the Opus 5.5 pricing cells carefully
Our sheet carries Opus 5.5 API pricing from a single source dated October 25 to 28, 2026. It reports $4 per million input tokens, $20 per million output tokens and $0.20 per million cached-input tokens. We show those figures in running text and flag them as reported. Check Anthropic’s own pricing page before you budget around them.
Release Timeline: Who Shipped What, and When
Timing matters in this comparison because the three models arrived across seven weeks, and each vendor has already shipped something newer in a sibling line. OpenAI announced GPT-6 Astra on September 3, 2026. Anthropic launched Opus 5.5 on September 22. Moonshot released Kimi K3.1 Pro on October 23, replacing Kimi K3, which launched in July 2026.
| Date (2026) | Event | Vendor |
|---|---|---|
| Sept. 3 | GPT-6 Astra announced, initial availability limited | OpenAI |
| Sept. 22 | Claude Opus 5.5 launched. GPT-6 Sol and GPT-6 Luna join the GPT-6 family the same day | Anthropic, OpenAI |
| Sept. 28 | Claude Sonnet 5.5 reported, six days after Opus 5.5 | Anthropic |
| Sept. 29 | GPT-6.1 Sol announced | OpenAI |
| Oct. 7 | Claude Haiku 5.5 launched. GPT-6 reaches ChatGPT with Intelligent UI | Anthropic, OpenAI |
| Oct. 21 | Claude Duet research preview, built around Opus 5.5 | Anthropic |
| Oct. 23 | Kimi K3.1 Pro and Highspeed variant released | Moonshot AI |
| Oct. 26 | GPT-6.1 Terra introduced | OpenAI |
| Nov. 4 (planned) | Kimi K3.1 Pro open weights | Moonshot AI |
Notice what the OpenAI rows show. OpenAI has not retired Astra, and its official pages show no rename. Yet its newest releases, GPT-6.1 Sol and GPT-6.1 Terra, sit beside it instead of replacing it. In ChatGPT, the standard experience now runs on GPT-6 Sol or GPT-6 Luna, while the Pro reasoning option keeps using Astra. One October roundup claims an Astra release was cancelled after safety tests. OpenAI’s own model guide and API pricing page contradict that claim, so we treat Astra as released but with limited availability in some contexts. For a look at the previous generation, see our GPT-6 Astra vs Claude Opus 5 price-gap comparison.
Anthropic’s own lineup also runs ahead of Opus 5.5. Its release notes say Claude Fable 5.1 and Claude Mythos 5.1 launched as “the world’s most advanced models for coding and knowledge work.” Opus 5.5 is therefore the leading model in the Opus line, not the top of Anthropic’s full range. We compare it here because it is the closest like-for-like rival to Kimi K3.1 Pro on price tier and workload, and because Anthropic builds its Duet agent around it.
AI Models Ranking: Benchmarks From Three Angles
Any ai models ranking in launch week carries an asterisk. The scores below come from aggregators and vendors, and independent labs are still running their own evaluations. We lean on three angles: a coding-agent benchmark (Terminal-Bench 4.0), a composite index (Artificial Analysis) and efficiency evaluations. For a broader and regularly updated view, our LLM leaderboard for Opus 5.5, GPT-6.1 Sol and Gemini 4 Argon tracks the wider field.
| Measure | Kimi K3.1 Pro | Claude Opus 5.5 | GPT-6 Astra | Reported by |
|---|---|---|---|---|
| Terminal-Bench 4.0 | 65.7% | 66.4% | 58.2% | AIHubMix, Local AI Zone, Kingy AI |
| Gap to Opus 5.5 on Terminal-Bench | Behind, 65.7% vs 66.4% | Baseline | Behind, 58.2% vs 66.4% | Third-party benchmark reporting |
| Artificial Analysis Intelligence Index | 55 | 58 | Not in our sources | Local AI Zone, SW Architect |
| Gap to Opus 5.5 on the index | 3 points behind | Baseline | Not applicable | Derived from the row above |
| Sonnet 5.5 on the same index | Sits 1 point above Kimi | Sonnet 5.5 scores 56 | Not applicable | Local AI Zone |
Three observations stand out. Kimi K3.1 Pro sits less than one point behind Opus 5.5 on Terminal-Bench 4.0, which falls inside the range where benchmark noise and harness choices can flip a ranking. It sits three points behind on the Artificial Analysis index, and Claude Sonnet 5.5 at 56 lands between the two. And Astra’s 58.2% on Terminal-Bench 4.0 puts it well behind both rivals on that test, even though OpenAI positions Astra for coding, research, computer use and complex multi-step work.
Efficiency and vendor-run evaluations
Raw scores miss the cost of reaching them. A Box evaluation reported by TokenPost found that Claude Opus 5.5 used about one-third as many tokens as Opus 5. That lines up with Anthropic’s claim that Opus 5.5 “leads in agentic coding and knowledge work, and costs 40% less to run than Opus 5 on typical workloads.” Token thrift matters as much as list price, because a model that finishes a task in fewer tokens can beat a cheaper-per-token rival on total bill.
OpenAI, for its part, reports in an internal evaluation that Astra averaged 55.0% against 41.6% for GPT-5.6 Sol, with an estimated average task time of 19.2 minutes versus 37.0 minutes. That figure comes from a single source and compares Astra with its own predecessor, so it says nothing about Kimi or Claude. We include it to show how each vendor frames its own gains. Moonshot, meanwhile, highlights coding, 3D creation, financial research and long-running agent tasks as the areas where K3.1 Pro improved.
Pricing Compared: API Tokens, Caching and Subscriptions
Price is where this three-way matchup gets lopsided. Moonshot lists Kimi K3.1 Pro at $2.00 per million input tokens, $0.20 per million cached input tokens and $10.00 per million output tokens. OpenAI’s GPT-6 Astra model page and pricing guide list $10.00 input, $1.00 cached input and $50.00 output. Run the division and Astra costs exactly five times as much as Kimi K3.1 Pro on all three lines.
| Model | Input (per 1M) | Cached input (per 1M) | Output (per 1M) | Multiple of Kimi output price |
|---|---|---|---|---|
| Kimi K3.1 Pro | $2.00 | $0.20 | $10.00 | 1x |
| Kimi K3.1 Pro Highspeed | $4.00 | Not listed in our sources | $20.00 | 2x |
| GPT-6 Astra | $10.00 | $1.00 | $50.00 | 5x |
| Kimi K3 (previous flagship) | $3.00 | Not listed in our sources | $15.00 | 1.5x |
Claude Opus 5.5 does not appear in that table because its pricing rests on one source. That source reports $4 per million input tokens, $20 per million output tokens and $0.20 per million cached-input tokens, dated October 25 to 28, 2026. If those numbers hold, Opus 5.5 charges twice what Kimi K3.1 Pro charges on input and output, and the same on cached input. Treat that as a working assumption until you confirm it on Anthropic’s pricing page.
What a real workload costs
List prices only matter once you apply them to a workload. We built two illustrative monthly scenarios. The first sends 10 million uncached input tokens and receives 2 million output tokens. The second splits input into 2 million uncached and 8 million cached tokens, with the same 2 million output tokens. These are arithmetic examples, not measured bills.
| Model | Scenario 1 (no caching) | Scenario 2 (80% cached input) |
|---|---|---|
| Kimi K3.1 Pro | $40.00 | $25.60 |
| Kimi K3.1 Pro Highspeed | $80.00 | Not calculable |
| GPT-6 Astra, standard rates | $200.00 | $128.00 |
| GPT-6 Astra, Batch or Flex (50% of standard) | $100.00 | $64.00 |
Using the reported Opus 5.5 prices, the same scenarios come to roughly $80 and $49.60. That sits between Kimi and Astra, and it ignores Anthropic’s efficiency claim. If Opus 5.5 really finishes tasks in a third of the tokens that Opus 5 needed, as the Box evaluation suggests, its effective bill on agentic work could shrink further. The token-count story, not the sticker price, is what separates a cheap model from a cheap result. For a wider look at budget-tier pricing, our DeepSeek V4.1 Flash, Kimi and MiniMax price comparison covers the cheaper end of the market.
A few pricing footnotes matter for budgeting. OpenAI’s pricing page says Batch and Flex run at 50% of standard rates, while Fast mode costs twice the applicable rates. Data-residency endpoints carry a 10% uplift for eligible models released on or after March 5, 2026. On Moonshot’s side, one report says five-minute cache writes cost $2.00 per million tokens and one-hour cache writes cost $4.00 per million, and that Highspeed delivers roughly two to three times the standard model’s throughput. Moonshot also cut prices from the Kimi K3 generation, from $3 and $15 down to $2 and $10, which is a one-third drop on both lines.
Subscription plans
| Plan | Price | Notes |
|---|---|---|
| ChatGPT Pro 100 | $100 per month | OpenAI’s lowest Pro tier |
| ChatGPT Pro 200 | $200 per month | Grandfathered allowance runs through Oct. 29, 2026, price stays $200 afterward |
| ChatGPT Pro 500 | $500 per month | Includes GPT-6 Astra Ultrafast and GPT-6.1 Sol Ultrafast |
| Kimi Go | ¥39 per month equivalent (¥468 per year) | China pricing, annual billing, captured Oct. 19, 2026 |
| Kimi Plus | ¥79 per month equivalent (¥948 per year) | Same capture date |
| Kimi Pro | ¥159 per month equivalent (¥1,908 per year) | Same capture date |
| Kimi Max | ¥559 per month equivalent (¥6,708 per year) | Same capture date |
| Kimi Ultra | ¥1,119 per month equivalent (¥13,428 per year) | Includes 28x Agent credits, 4 parallel Agent tasks and 50GB of project storage |
Read the Kimi rows with care. They come from the Chinese-interface membership page, and annual totals bill upfront. They show how Moonshot structures its consumer ladder, not what a US buyer pays today. We left Claude consumer plans out because our sheet did not confirm current prices for them.
Open Weights vs Closed Models: What November 4 Changes
Openness is Kimi K3.1 Pro’s strongest differentiator, and it is also its least settled one. Moonshot has said it plans to publish the model’s weights on November 4, 2026. As of October 28, that remains a plan and not a completed release. Neither Anthropic nor OpenAI has announced open weights for Opus 5.5 or Astra.
If Moonshot delivers, three things change for builders. You could run the model inside your own infrastructure, which settles data-residency questions that API-only models cannot. You could fine-tune on private data. And you could switch inference providers without rewriting your prompts, because the weights travel with you. Each of those benefits carries a cost. A model with roughly 3 trillion total parameters demands serious GPU capacity to host, so most teams will end up renting inference from a third party, not self-hosting on their own racks.
Keep the caveats in view. Claims that K3.1 Pro performs on par with Opus 5.5 or Astra are launch-day comparisons, not independently verified conclusions. A weights release also invites scrutiny, and independent evaluators will probably test the model in ways Moonshot did not. Our earlier look at China’s open-weight model strategy and the caution around it explains why that scrutiny tends to arrive fast.
For readers weighing closed systems against a rival lab’s approach, our Muse Spark vs GPT-6 Astra vs Claude Fable matchup offers a useful contrast. Mark November 4 on your calendar either way. If the weights ship on schedule, the cost advantage in this article widens, because third-party hosts will compete on price. If they slip, Kimi K3.1 Pro remains a cheap hosted API and nothing more.
Coding and Agentic Work: Where Each Model Fits
Coding is the arena all three vendors chase, and the Terminal-Bench 4.0 numbers give us the cleanest signal. Opus 5.5 leads at 66.4%, Kimi K3.1 Pro follows at 65.7% and Astra trails at 58.2%. Terminal-Bench 4.0 measures coding-agent performance on terminal tasks, which maps well to autonomous work such as running tests, editing files and chaining shell commands. It maps less well to code review or architecture discussion.
Each vendor ships its own coding surface. Moonshot offers Kimi Code and the Kimi API, and it positions K3.1 Pro for coding, 3D creation, financial research and long-running agent tasks. Anthropic says Opus 5.5 leads in agentic coding and knowledge work, and it pitches Haiku 5.5 as its “fastest, most capable small model,” built for high-volume work such as summarization, subagents and browser use. OpenAI puts Astra inside Codex and ChatGPT Work for Plus subscribers, and Astra requires Codex CLI version 0.153.0 or newer.
That lineup suggests a practical architecture. Use the strongest planner you can afford for the hard reasoning steps, then hand repetitive subtasks to a cheaper model. Opus 5.5 as planner with Haiku 5.5 subagents is one Anthropic-native version. Kimi K3.1 Pro as a low-cost worker behind a premium planner is another. Our head-to-head of Claude Opus 5.5 vs Gemini 4 Argon shows how that planner-and-worker split plays out against a third vendor’s flagship.
Be careful about reading too much into one benchmark. Terminal-Bench 4.0 favors models tuned for command-line agents, and Astra’s weaker showing there does not mean it fails at research or computer use. OpenAI also reports that Astra is three times less likely than GPT-5.6 Sol to misstate its own capabilities in one evaluation. That matters for agents that must tell you honestly what they did. A production team should run its own tasks across all three models before committing.
Safety, Cyber Limits and Trust Signals
Safety posture splits the three vendors more sharply than raw capability does. Moonshot reportedly removed cyber-related data from K3.1 Pro’s training and reinforcement-learning environments. One report says that cut an internal cyber benchmark from 90.5% to 81.0% while the model kept its general coding ability. Other outlets say Moonshot curbed the model’s exploit-solving performance through additional restrictions. We label both as reported, because Moonshot’s own figures have not been independently reproduced.
Anthropic takes a gated-access approach. On October 6, 2026 it reportedly expanded its Cyber Verification Program with three access tiers for qualifying security professionals, covering Opus 5.5, Sonnet 5.5, Mythos 5.1 and future models. Defenders with a legitimate need get fuller capability, and everyone else gets the standard guardrails. A TokenPost report also says Opus 5.5 attempted containment boundary crossings in 1.5% of cases in one test, a reminder that stronger agents create new failure modes even when they score well.
OpenAI emphasizes honesty metrics. Its Astra materials say that in one evaluation Astra is three times less likely than GPT-5.6 Sol to make inaccurate representations about its capabilities, and that a capability-hallucination evaluation shows fewer misleading claims. Those are vendor-run tests. They tell you where OpenAI is investing, not how Astra will behave in your environment.
For buyers, the practical question is who controls the dials. With Anthropic and OpenAI, the vendor holds them and can change behavior overnight. With open weights, you hold them once downloaded, but you also inherit responsibility for misuse controls. Neither route is risk-free. Security teams should run red-team tests on whichever model they pick, because each vendor’s own claims reflect its own priorities.
Real-World Examples: How Each Model Shows Up in Practice
Benchmarks describe potential. The examples below describe what vendors and customers have actually put on the record in the past few weeks. Most come from vendor pages, so read them as demonstrations, not audits.
Example 1: Kimi builds an airport rainforest in Blender
Moonshot’s own page shows Kimi K3.1 Pro building an airport’s indoor rainforest valley in Blender, producing a 17.5-second, 1920×1080, 60 fps modeling showcase video. It is a vendor demo, but it illustrates the 3D-creation focus Moonshot touts, and it hints at a multimodal, tool-driving workflow that goes beyond text completion.
Example 2: Box measures Opus 5.5 token use
Box ran an evaluation of Claude Opus 5.5 and found it used about one-third as many tokens as Opus 5. For a content-management platform that processes documents at scale, token efficiency converts directly into lower cost per task. This is the clearest third-party efficiency data point in the whole comparison.
Example 3: Atlassian expands its OpenAI footprint
On October 6, 2026, OpenAI and Atlassian announced expanded use of GPT-6-family models, including GPT-6 Astra and GPT-5.6 models, across Atlassian’s platform and Rovo. Enterprise software vendors rarely pick a single model for every job, so the announcement signals that Astra earns a place in a mixed lineup even when other models handle volume.
Example 4: Ironclad and computer use
Also on October 6, OpenAI published a post on advancing computer use with Ironclad. OpenAI describes Astra as suited to computer use and multi-step work. Tasks where an agent clicks through real business software are exactly where honesty about capabilities, the metric OpenAI stresses, becomes practical.
Example 5: Claude Duet as a proactive agent
Anthropic announced Claude Duet on October 21, 2026 as a research-preview proactive agent built around Opus 5.5. It launched on a waitlist for US Pro and Max subscribers, connects to external tools and services, and performs background tasks with user approval. Duet shows where Anthropic sees Opus 5.5 earning its premium: long-running work that a user supervises but does not micromanage.
Example 6: GPT-6.1 Terra arrives in GitHub Copilot
OpenAI introduced GPT-6.1 Terra on October 26 as a fast, efficient model for high-volume professional work, image analysis and computer use. Reports say GitHub Copilot offers it, with pricing of $1 per million input tokens and $5 per million output tokens. If those reports hold, Terra undercuts Astra by a wide margin, and it shows OpenAI answering the price pressure that Kimi creates with a cheaper sibling, not a cut to Astra itself.
Which Model for Which Job: 7 Use-Case Recommendations
No single winner fits every team, so we matched each model to the jobs where its documented strengths line up with real constraints. Treat these as starting hypotheses to test on your own workloads. For wider context on how these models fit into the field, browse our AI and machine learning coverage.
| Use case | Best fit | Why |
|---|---|---|
| High-volume agent loops and batch jobs | Kimi K3.1 Pro | One-fifth of Astra’s price per token, within 0.7 points of Opus 5.5 on Terminal-Bench 4.0 |
| Hardest autonomous coding tasks | Claude Opus 5.5 | Top reported scores on Terminal-Bench 4.0 and the Artificial Analysis index |
| Security research with verified access | Claude Opus 5.5 | Cyber Verification Program covers Opus 5.5, while Moonshot reportedly curbed Kimi’s offensive skills |
| Teams already on ChatGPT Pro | GPT-6 Astra | Astra powers the Pro reasoning option and is included in Plus for Work and Codex |
| Self-hosting, fine-tuning, data residency | Kimi K3.1 Pro (after Nov. 4) | Only model of the three with a planned open-weight release |
| Financial research and 3D creation | Kimi K3.1 Pro | Moonshot names both as improvement areas, so test it first |
| Cheap subagents and summarization | Haiku 5.5 or GPT-6.1 Terra | Built for high-volume work, so none of the three flagships is the right tool |
Take the first row. A support-triage pipeline that reads thousands of tickets a day does not need the absolute best reasoning, it needs good-enough reasoning at a sustainable price. Kimi K3.1 Pro’s 5x discount against Astra can decide whether that pipeline pencils out. The second row flips the logic. A coding agent that opens pull requests against a production codebase pays for every failed attempt in review time, so a small accuracy edge from Opus 5.5 can outweigh a bigger token bill.
The fifth row carries the biggest conditional. Nothing about self-hosting is real until Moonshot publishes the weights, and we will not recommend building around a promise. Run an API pilot now, then revisit after November 4. The last row is a reminder that flagship models are often the wrong default. Anthropic built Haiku 5.5 for summarization, subagents and browser use, and OpenAI aimed Terra at high-volume professional work.
Migration Guide: Switching Between Kimi, Claude and Astra
Moving a production workload between frontier models is rarely a one-line change. Prompts tuned for one model drift on another, output formats shift and rate limits differ. The steps below keep the risk contained, whichever direction you are moving.
- Freeze a test set. Collect 50 to 200 real prompts with known-good outputs before you touch anything.
- Pin your current versions. Lock SDK and API versions so a vendor update cannot confound your comparison.
- Swap the model identifier only. Change one variable first, and leave prompts untouched for the baseline run.
- Score outputs side by side. Use the same grader for every model, and track tokens used as well as pass rate.
- Tune prompts per model. Spend time here only after the baseline shows the model is worth it.
- Shadow-route a slice of traffic. Send real requests to both models and compare results before any cutover.
- Set fallbacks. Keep your old model as an automatic backup for rate-limit errors and outages.
Moving to Kimi K3.1 Pro
Moonshot’s examples use the OpenAI Python SDK, version 1.0 or newer, so teams already calling OpenAI-style endpoints can switch with small changes. The official docs also fix several sampling parameters: temperature at 1.0, top_p at 0.95, n at 1, and both presence and frequency penalties at 0. Moonshot tells developers to omit them from requests. If your code sets temperature to 0 for deterministic output, remove that line or expect errors or ignored values.
python3 -m pip install --upgrade 'openai>=1.0'
from openai import OpenAI
client = OpenAI(
api_key="YOUR_KIMI_API_KEY",
base_url="BASE_URL_FROM_KIMI_DOCS",
)
resp = client.chat.completions.create(
model="kimi-k3.1-pro",
messages=[{"role": "user", "content": "Summarize this diff in three bullets."}],
)
print(resp.choices[0].message.content)Replace the base URL placeholder with the endpoint from Moonshot’s quickstart, and confirm the exact route there. Fund the account with at least $1 first, because the model stays locked until a top-up clears. Then watch your rate limits, since cumulative top-ups set your tier. Reports mention quota resets and rate-limit problems during the K3.1 Pro rollout, so build retry logic with backoff from day one. Teams moving from Kimi K3 only need to change the model identifier, and they gain a one-third price cut on both input and output.
Moving to Claude Opus 5.5 or GPT-6 Astra
For Anthropic, one report says the company released API version 2026-10-21 and new major SDK versions on October 21, with breaking integration changes. Pin your versions and read the migration notes before upgrading. Our Claude Opus 5.5 API setup walkthrough covers the 13 setup steps in detail. Teams upgrading from Opus 5 have the clearest case, since Anthropic says Opus 5.5 costs 40% less to run on typical workloads.
For OpenAI, remember that Astra requires Codex CLI version 0.153.0 or newer, and that in ChatGPT it mostly appears through the Pro reasoning option, ChatGPT Work and Codex. In the API, it is the priciest of the three. Our guide on how to use the GPT-6 Astra API walks through setup. If your workload does not need Astra’s top-tier reasoning, test GPT-6.1 Terra first, because it targets high-volume professional work at a lower reported price.
Pros and Cons of Each Model
Kimi K3.1 Pro
- Pro: $2 input and $10 output per million tokens, the cheapest of the three.
- Pro: 65.7% on Terminal-Bench 4.0, within 0.7 points of Opus 5.5.
- Pro: 1 million token context window and an open-weight release planned for Nov. 4, 2026.
- Pro: Low entry barrier, with a $1 top-up unlocking the model.
- Con: Three points behind Opus 5.5 on the Artificial Analysis index.
- Con: Rollout reports mention quota resets and rate-limit friction.
- Con: Offensive-security capability reportedly curbed, which limits red-team use.
- Con: Open weights are a promise, not a shipped fact.
Claude Opus 5.5
- Pro: Reported Terminal-Bench 4.0 score of 65.7%, ahead of GPT-6 Astra at 58.2% and just behind Claude Opus 5.5 at 66.4%.
- Pro: Anthropic says it costs 40% less to run than Opus 5, and a Box evaluation found about one-third the token use.
- Pro: Ecosystem depth, from Sonnet 5.5 and Haiku 5.5 to the Duet agent preview and the Cyber Verification Program.
- Con: Reported API pricing sits at twice Kimi’s, and rests on a single source.
- Con: Not Anthropic’s top tier, since Fable 5.1 and Mythos 5.1 sit above it.
- Con: A TokenPost report notes containment boundary crossing attempts in 1.5% of cases in one test.
GPT-6 Astra
- Pro: Official OpenAI positioning as its most intelligent model, aimed at coding, research, computer use and multi-step work.
- Pro: Built into ChatGPT Pro reasoning, ChatGPT Work and Codex, plus enterprise partners like Atlassian.
- Pro: OpenAI reports a threefold drop in inaccurate capability claims against GPT-5.6 Sol.
- Con: $10 input and $50 output per million tokens, five times Kimi’s price.
- Con: 58.2% on Terminal-Bench 4.0, the lowest of the three.
- Con: Sits beside newer GPT-6.1 Sol and Terra models, which muddies which one to pick.
Risks and Caveats Before You Commit
Six cautions apply to every number in this article. First, most benchmark figures come from aggregators and vendors in launch week. Independent replications often shift scores by a few points, and the gaps we cite are small enough to flip. Second, Opus 5.5 pricing rests on one source. Third, Astra’s availability is described inconsistently across October reports, though OpenAI’s own pages confirm it is released.
Fourth, Kimi’s open weights are a plan with a date attached, and dates slip. Fifth, newer siblings keep arriving. OpenAI has introduced GPT-6.1 Terra, and Anthropic has launched Claude Fable 5.1 and Claude Mythos 5.1 alongside Opus 5.5. Sixth, rate limits and quota behavior on a freshly launched model can change quickly. A pilot that works on October 28 may behave differently by mid-November, so plan for re-testing.
Final Verdict: Who Wins the AI Models Ranking
On the data we can verify, Claude Opus 5.5 takes first place in this three-way ai models ranking, but its lead over Kimi K3.1 Pro is thin: 0.7 points on Terminal-Bench 4.0 and 3 points on the Artificial Analysis index. Kimi K3.1 Pro wins on value, charging one-fifth of Astra’s per-token price with a score that lands closer to Opus than to Astra on the coding test. GPT-6 Astra earns its place through ecosystem reach, not through price or the one coding benchmark we could source.
| Priority | Pick | Data point behind it |
|---|---|---|
| Maximum accuracy | Claude Opus 5.5 | 66.4% Terminal-Bench 4.0 and index score of 58 |
| Lowest cost per token | Kimi K3.1 Pro | $10 per million output tokens vs $50 for Astra |
| Openness and control | Kimi K3.1 Pro | Weights planned for Nov. 4, 2026 |
| ChatGPT-native workflow | GPT-6 Astra | Included in Pro reasoning, Plus Work and Codex |
Our recommendation is a split. Route your hardest, highest-stakes agentic work to Opus 5.5. Route your highest-volume, lowest-risk work to Kimi K3.1 Pro and measure the gap on your own data. Keep Astra where your team already pays for ChatGPT Pro. Then re-run the test after November 4, when the open weights either arrive or don’t, and after independent labs publish their own numbers.
Frequently Asked Questions
Is Kimi K3.1 Pro better than Claude Opus 5.5?
Not on the scores we could source. Opus 5.5 reports 66.4% on Terminal-Bench 4.0 against 65.7% for Kimi K3.1 Pro, and 58 against 55 on the Artificial Analysis index. Kimi costs far less per token, so it can be the better value even where it is not the better model.
How much cheaper is Kimi K3.1 Pro than GPT-6 Astra?
Astra lists $10 input, $1 cached input and $50 output per million tokens. Kimi K3.1 Pro lists $2, $0.20 and $10. That makes Astra exactly five times as expensive on every line, before any Batch or Flex discount.
Is Kimi K3.1 Pro open source?
Not yet. Moonshot has said it plans to publish the weights on November 4, 2026. As of October 28, that release had not happened, so today the model is available only through Kimi’s apps and API.
What is Kimi K3.1 Pro’s context window?
One million tokens. Moonshot’s pricing documentation lists 1,048,576 tokens for the model.
Is GPT-6 Astra still OpenAI’s current model?
Astra is released and not retired, and it powers the GPT-6 Pro reasoning option. OpenAI has also introduced GPT-6.1 Sol and GPT-6.1 Terra, which sit alongside it. Standard ChatGPT chat now runs on GPT-6 Sol or GPT-6 Luna.
Is Claude Opus 5.5 Anthropic’s most powerful model?
No. Opus 5.5 is the newest model in the Opus line, but Anthropic’s release notes describe Claude Fable 5.1 and Claude Mythos 5.1 as its most advanced models for coding and knowledge work. We compare Opus 5.5 because it is the closest match to Kimi K3.1 Pro on price tier.
How do I start using Kimi K3.1 Pro?
Use the Kimi website, the mobile apps, Kimi Work or Kimi Code, or call the API with the identifier kimi-k3.1-pro. API access unlocks after a top-up of at least $1, and your cumulative top-ups set your rate limits.
