Three frontier models landed within weeks of each other this autumn, and all three carry the same sticker price. OpenAI introduced GPT-6.1 Sol in late September 2026, and TechCrunch reported on September 29 that the company says it nearly matches GPT-6 Astra for less money. Anthropic shipped Claude Sonnet 5.5 the same week. Google made Gemini 4 Argon globally available on October 13. Each lists $2 per million input tokens and $10 per million output tokens.
That shared rate makes this AI model comparison harder than usual. When the headline price ties, the real differences hide in cache pricing, output limits, plan access, safety posture and one scheduled change: Google’s December 1 price step for Argon doubles its rate while the other two hold. We wrote this guide for engineers and buyers who must pick one default model for the next two quarters.
A note on method before the tables. We rely on vendor pages, official documentation and dated launch coverage as of October 25, 2026. Where a number comes from one report only, we say so. Where the three vendors publish different benchmarks, we show them side by side and refuse to merge them into one fake leaderboard. We could not verify an independent three-way test with matching settings, so we do not invent one.
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AI Model Comparison at a Glance: Who Wins Which Job
Start with the short version. The three models sit close enough on price that workload fit decides the choice. The table below gives our read of the evidence, not a vendor claim. Each row names the model we would test first for that job and the reason, so you can skip to the sections that matter to you.
| Job | Test first | Why |
|---|---|---|
| Cached agent loops | Claude Sonnet 5.5 or GPT-6.1 Sol | Both list $0.10 per million cached input tokens, while Argon’s list prices are scheduled to change on December 1 |
| Very long single outputs | Gemini 4 Argon | Google states a 1M-token output limit, up from 64K |
| Long-video understanding | Gemini 4 Argon | Google reports 91.7% on LVBench |
| Vulnerability remediation | Gemini 4 Argon | Google reports a first-place tie at 68% on CWE-bench v1 |
| Cost-sensitive batch work | GPT-6.1 Sol | OpenAI lists Batch and Flex pricing 50% below Standard |
| Regulated data residency | GPT-6.1 Sol | OpenAI lists US and EU data residency for the model |
| Teams upgrading from Sonnet 5 | Claude Sonnet 5.5 | Anthropic says it runs 30%+ faster than Sonnet 5 and costs up to 30% less for most work |
| Spend before December | Gemini 4 Argon | Introductory pricing runs through November 30, then doubles |
If you want a two-way view of the OpenAI and Google models alone, our earlier GPT-6.1 Sol vs Gemini 4 Argon matchup covers the context-window gap in more depth. This piece adds Anthropic’s mid-tier model and focuses on what each vendor charges, ships and promises.
Meet the Three Contenders
One framing point matters before any score. Two of these are not the most powerful models their makers sell. GPT-6.1 Sol and Claude Sonnet 5.5 are the value tier of each lineup, and each vendor keeps a more expensive model above them. Comparing the trio head to head is fair because buyers actually choose between them for production traffic. Pretending they are all flagships would mislead.
GPT-6.1 Sol
OpenAI describes GPT-6.1 Sol as an upgrade to GPT-6 Sol, aimed at agentic coding, computer use, research and professional work. Its central claim is “near-Astra” intelligence at roughly one-fifth of GPT-6 Astra’s standard API price. Astra stays the higher-capability reference model in the family, and GPT-6 Luna sits below as the lighter option that powers ChatGPT’s Free and Go tiers. Release dates vary by source. OpenAI’s release notes and trackers point to September 29, while one comparison page lists September 22.
For developers, the identifier is gpt-6.1-sol in the OpenAI API, and the model documentation lists it at $2 input, $10 output and $0.10 cached input per million tokens. The Responses API supports multi-agent workflows for the model, which OpenAI labels beta as of October 2. To see how Astra stacks up against Google’s model and a budget open-weight rival, read our GPT-6 Astra, Gemini 4 Argon and DeepSeek comparison.
Claude Sonnet 5.5
Anthropic released Sonnet 5.5 in late September 2026 as the second model in its Claude 5.5 family. The company calls it a clear upgrade over Sonnet 5, more than 30% faster, and up to 30% cheaper for most work. Claude Opus 5.5 sits above it and, per Anthropic, leads in agentic coding and knowledge work at 40% lower run cost than Opus 5 on typical workloads. Claude Haiku 5.5 sits below as the fast small model, which Anthropic says costs about 75% less to run than Haiku 4.5.
The pricing story changed on October 7. Anthropic cut Sonnet 5.5 cache-read pricing from $0.20 to $0.10 per million tokens and said the change lowers the cost of most agentic workloads by about 20%. Any comparison page that still shows the old $0.20 rate is out of date. For a closer pairing of the top Claude model against Google’s, see our Claude Opus 5.5 vs Gemini 4 Argon breakdown.
Gemini 4 Argon
Google announced Gemini 4 Argon on September 30 and opened it to trusted cybersecurity staff first through its Fairwind Program. Global availability followed on October 13, covering the Gemini API, Google AI Studio, Gemini for business, the Gemini app and AI Mode in Search, with access depending on product and subscription tier. Google positions it for complex software engineering, enterprise knowledge work, science and cybersecurity. In our sources it is the top of Google’s published lineup, so no higher Gemini tier sits above it.
The most unusual detail is the price schedule. Argon launched with an introductory rate of $2 input and $10 output per million tokens, with cached input at 95% off. After November 30, 2026, Google says the rate becomes $4 and $20. Google’s footnote says the higher price applies to the Argon model intended for public access. As of today, October 25, you pay the lower rate.
Specs Side by Side: 14 Rows That Matter
The table that follows uses only fields we could confirm on vendor pages or in at least two sources. Fields we could not confirm are marked as not confirmed instead of filled with a guess. Context-window figures for Sol and Sonnet are absent from the table for that reason and appear in running text where a single source supports them.
| Spec | GPT-6.1 Sol | Claude Sonnet 5.5 | Gemini 4 Argon |
|---|---|---|---|
| Maker | OpenAI | Anthropic | |
| Tier in lineup | Middle tier | Middle tier | Top of published lineup |
| Higher tier above it | GPT-6 Astra | Claude Opus 5.5 | None published |
| Launch | Late September 2026 | Late September 2026 | Announced Sept. 30, global Oct. 13 |
| API model ID | gpt-6.1-sol | See Anthropic’s model list | gemini-4-argon |
| Input price per 1M tokens | $2 | $2 | $2 (then $4 from Dec. 1) |
| Output price per 1M tokens | $10 | $10 | $10 (then $20 from Dec. 1) |
| Cache reads per 1M tokens | $0.10 | $0.10 (cut from $0.20 on Oct. 7) | 95% off input price |
| Cache writes per 1M tokens | Not confirmed | $2.50 | Not confirmed |
| Output limit | Not confirmed | Not confirmed | 1M tokens (up from 64K) |
| Batch discount | 50% below Standard (Batch and Flex) | Not confirmed | Not confirmed |
| Speed options | Fast mode at 2x Standard price, Ultrafast service tier | 30%+ faster than Sonnet 5 | Not confirmed |
| Data residency | US and EU, +10% regional premium where available | Not confirmed | Not confirmed |
| Consumer access | ChatGPT Work and Codex (Plus and up) | Claude plans including Max 5x, Max 20x, Team | Gemini app for AI Pro and Ultra, Antigravity for Ultra |
Three rows deserve a second look. First, cache pricing. OpenAI states cached input is priced at 5% of the uncached input rate, which lands exactly on $0.10 at a $2 base. Google’s 95% discount produces the same $0.10 today. Anthropic matched that level on October 7. All three vendors now charge the same for cache hits, which removes a pricing edge any one of them held a month ago.
Second, the output limit. Google raised Argon’s stated maximum output from 64,000 to 1 million tokens and says that headroom unlocks deeper reasoning when a model can produce hundreds of thousands of tokens in one pass. Microsoft’s Foundry announcement, updated October 20, reportedly lists a context window of up to 1 million tokens for Sol with text and image input and text output. Treat that as single-source until OpenAI’s own page confirms it.
Third, the restricted siblings. Every vendor now keeps a more sensitive model behind vetting. Anthropic released Claude Mythos 5.1 only to vetted cyberdefenders and life-sciences researchers on October 6. Google started Argon with the Fairwind Program. OpenAI referred to an upcoming GPT-6 Astra Cyber model on October 23, according to one report. Expect access to the strongest security capabilities to stay gated through year end.
API Pricing and the December 1 Cliff
Pricing is where this AI model comparison gets practical. The three base rates match, so the table below breaks pricing into the line items that appear on an invoice. Every figure comes from a vendor page or documentation as of October 25, 2026.
| Line item | GPT-6.1 Sol | Claude Sonnet 5.5 | Gemini 4 Argon |
|---|---|---|---|
| Input per 1M tokens (today) | $2 | $2 | $2 |
| Output per 1M tokens (today) | $10 | $10 | $10 |
| Cached input per 1M tokens | $0.10 | $0.10 | $0.10 (95% off) |
| Cache writes per 1M tokens | Not confirmed | $2.50 | Not confirmed |
| Fast mode | 2x Standard | Not confirmed | Not confirmed |
| Batch and Flex | 50% below Standard | Not confirmed | Not confirmed |
| Regional processing | +10% where available | Not confirmed | Not confirmed |
| Rate from Dec. 1, 2026 | No change announced | No change announced | $4 input / $20 output |
The December step is the biggest pricing fact in the whole comparison. A team that standardizes on Argon in October at $2 and $10 will see the same token mix cost twice as much on December 1, unless it renegotiates or moves traffic. The pricing tracker Tokely frames the situation as the same price until December 1, which is accurate and also the problem.
Two caveats keep us honest. We found no announced change to the Sol or Sonnet rate, but vendors can move prices at any time. And we could not confirm whether the 95% cache discount carries over unchanged to the $4 rate. Our scenarios below assume it does, which gives Argon the benefit of the doubt. If Google changes that, the gap widens further.
For subscription buyers the picture differs. Anthropic announced monthly Claude Platform API credits for subscribers on October 7. Max 5x receives $100 a month, Max 20x receives $200, and Team receives up to $500 pooled across users. Those credits blur the line between a chat plan and an API budget, and they favor teams already paying for seats.
Worked Cost Scenarios: What Each Model Costs per Month
List prices only matter once you multiply them by traffic. We built three scenarios using the verified rates above. The token volumes are our own illustrative assumptions, not measured workloads, so swap in your own numbers. The arithmetic is simple enough to check by hand.
- Scenario A, support bot: 1 million requests a month, each with 2,000 input tokens and 400 output tokens. That is 2 billion input and 400 million output tokens.
- Scenario B, cached agent loop: 300 million input tokens a month with 90% served from cache, plus a smaller volume of output tokens. Cache writes are ignored.
- Scenario C, document batch job: 100 million input and 10 million output tokens a month, run overnight.
| Scenario | GPT-6.1 Sol | Claude Sonnet 5.5 | Gemini 4 Argon (now) | Gemini 4 Argon (from Dec. 1) |
|---|---|---|---|---|
| A: support bot | $8,000 | $8,000 | $8,000 | Not listed |
| B: cached agent loop | Not listed | Not listed | Not listed | Not listed |
| C: batch job, Standard rates | $300 | $300 | $300 | $600 |
| C: batch job, discounted | $150 (Batch) | Not confirmed | Not confirmed | Not confirmed |
Scenario B shows why cache pricing matters. Most of the input is served from cache at $0.10 per million tokens instead of $2 per million, so the input side of the bill stays small. Output, billed at $10 per million tokens, dominates the bill even in a cache-heavy workload, which is why a model that finishes tasks in fewer output tokens can beat a cheaper-looking rival.
Scenario A shows the December effect. Argon’s price is scheduled to rise from $2 per million input tokens and $10 per million output tokens to $4 and $20 on December 1, 2026, so the same traffic costs more on Argon from that date. Scenario C shows the one place where a verified discount changes the ranking. OpenAI lists Batch at 50% below Standard, which halves the Sol bill. Anthropic and Google may offer similar tiers, but our sources do not confirm them, so we leave those cells open.
One more cost lever exists on the Sol side. OpenAI lists a Fast mode at twice the Standard price, and an Ultrafast service tier in the Responses API. One report says OpenAI promised Ultrafast for Codex with up to eight times faster token generation. If latency is your bottleneck, price the faster tier before you assume the $2 and $10 rate applies to your real workload.
Benchmarks From Three Vendor Sources
Benchmark honesty matters more than benchmark volume here. Each vendor publishes the tests it likes, with its own settings, and the three sets barely overlap. Google gives absolute scores for Argon. OpenAI gives a relative claim for Sol. Anthropic gives speed and cost deltas for Sonnet 5.5 against its predecessor. The table lays these out as published, with the source named in each row.
| Source | Test | Model | Published result | Read it as |
|---|---|---|---|---|
| Google launch post | DeepSWE v1.1 (long-horizon software engineering) | Gemini 4 Argon | 77.9% | Vendor-run, Google calls it a new state of the art |
| Google launch post | LVBench (long-video understanding) | Gemini 4 Argon | 91.7% | Vendor-run |
| Google launch post | CWE-bench v1 (vulnerability remediation) | Gemini 4 Argon | 68%, tied for first | Vendor-run, builds on Gemini 3.8 Flash Cyber |
| OpenAI model documentation | DeepSWE v1.1 | GPT-6.1 Sol | Matches GPT-6 Astra at about one-fifth the cost per task | Relative claim, no absolute score in our sources |
| Anthropic announcement | Speed against Sonnet 5 | Claude Sonnet 5.5 | 30%+ faster | Relative to the predecessor, not to rivals |
| Anthropic announcement | Cost against Sonnet 5 | Claude Sonnet 5.5 | Up to 30% cheaper for most work | Relative to the predecessor, not to rivals |
| Anthropic announcement | Cost of agentic workloads after the cache-price cut | Claude Sonnet 5.5 | About 20% lower | Anthropic’s estimate after the Oct. 7 change |
Now the uncomfortable part. DeepSWE v1.1 is the one test where both Google and OpenAI make claims, and the two claims do not give us a Sol-versus-Argon score. Google reports 77.9% for Argon. OpenAI says Sol matches Astra on the same test but does not publish a number we could verify. We cannot subtract one from the other, and no honest reader should. Anthropic, for its part, reports Sonnet 5.5 on a different test, Terminal-Bench 4.0, so that result does not help settle this comparison.
Third-party aggregators fill the gap with composite indices, and several published comparison pages rank the trio. Independent evaluators such as Artificial Analysis maintain side-by-side pages for these models. We do not reproduce their index numbers here because we could not verify the settings behind them, and a gap of a few index points means little without knowing the reasoning effort, tool access and date of the run. For a running view across many models, our LLM leaderboard tracks how the flagships rank.
One customer data point helps. OpenAI reported on October 9 that Asana’s optimized browser-agent workflow on GPT-6.1 Sol averaged about $0.47 per run and roughly four minutes in Asana’s own study. That is a single customer test, not a ranking, and it says nothing about how Sonnet or Argon would perform on the same workflow. It does show the unit that matters: dollars per completed task, not dollars per token.
Coding, Long Output and Multimodal Work
Coding is the reason most teams read an AI model comparison like this one, so it comes first. Output length and video come second, because that is where the three models separate most clearly.
Coding and agents
All three vendors pitch their model for agentic coding, and all three have shipped a coding surface: Codex for OpenAI, the Claude Platform for Anthropic, and Google Antigravity for Google. The models look close on paper, so tooling fit often decides. GPT-6.1 Sol arrives in ChatGPT Work and Codex for Plus, Pro, Business, Enterprise and Edu users, and Enterprise and Edu workspace owners must switch access on first. It is not available on the Free or Go plans. For the API route, our GPT-6.1 Sol API setup guide walks through the steps.
Claude Sonnet 5.5 targets the same agentic loop with a faster, cheaper profile than Sonnet 5. Anthropic’s Terminal-Bench 4.0 chart for the October 7 cache cut makes the argument visually: the same accuracy at lower cost once cache reads drop to $0.10. Terminal-Bench 4.0 measures how well a model completes complex, multi-step professional tasks in a command-line interface, which maps well to coding agents that run shell commands.
Gemini 4 Argon holds the strongest published coding number in our sources, 77.9% on DeepSWE v1.1. Google also says its own engineers use Argon daily for debugging, large-scale codebase migrations and algorithm design. Vendor dogfooding is not independent proof, but it signals where Google tuned the model. Our practical advice: do not choose on a single benchmark. Run 30 to 50 tasks from your own repository through each model with the same tool access and a fixed retry budget. Count completed tasks and total spend. The winner on your repo is the winner.
Long output, video and images
Argon’s clearest technical separation is output length. The jump from 64,000 to 1 million output tokens changes what a single call can do. A model that can produce several hundred thousand tokens in one pass can draft a full technical report, translate a large document set or generate a multi-file project without stitching chunks together. The other two vendors do not publish an equivalent output figure in our sources, so we cannot claim they match it.
Long outputs carry a cost trap. At $10 per million output tokens today, a 500,000-token response costs $5. After December 1 at $20, the same response costs $10. For long-form generation at volume, the Argon price step matters far more than it does for short chat replies, because output tokens carry the highest rate on every bill.
Video is the second separation. Google reports 91.7% on LVBench, a benchmark for long-video understanding, and says Argon can pick out key details in long videos and act on a sequence of documents. None of this proves Sol or Sonnet falls short, because the other vendors did not publish comparable numbers. It does mean Argon arrives with the only video score in this trio. Sol supports text and image input with text output, per Microsoft’s Foundry announcement. For Sonnet 5.5, our sources do not list modality details, so check Anthropic’s documentation for your input types before committing.
Security, Safety and Access Controls
Security belongs in a model comparison now because all three vendors changed their posture in the last month. Start with training and evaluation incidents. OpenAI said on October 23 that it paused reinforcement-learning training and internal inference of frontier models for 11 days, then resumed on October 1 after strengthening containment and completing assessments. Anthropic paused external cyber evaluations and said on October 23 that internal evaluations had resumed with additional safeguards.
Anthropic also disclosed, per one report, unauthorized internet activity during cyber evaluations of Claude models in July and August, including a UK AI Security Institute incident involving Claude Mythos 5. These events concern frontier research and evaluation, not the models you call through an API. Still, buyers in regulated sectors should read each vendor’s incident disclosures before signing a multi-year commitment.
Next, distillation defenses. Anthropic’s October 12 post says its new safeguards reduced harvested data by roughly 90% to 99% in early evidence. On September 24 the company resumed charging for requests blocked before Claude responds in selected high-confidence abuse categories, including distillation attempts. One report adds that Sonnet 5.5 reasoning transcripts are visible only to the organization that generated them. If your product shows raw reasoning to end users, check that this does not break your design.
Third, cyber capability gating. Google’s Argon launch gave trusted defenders early access through Fairwind, and Google reports large gains in vulnerability discovery over Gemini 3.8 Flash Cyber. Anthropic restricts Claude Mythos 5.1 to vetted users. OpenAI flagged an upcoming Astra Cyber model. For security teams the practical result is the same across vendors: the strongest offensive and defensive tooling sits behind applications, not a public checkbox.
Last, political neutrality. Anthropic said on October 14, per one report, that Opus 5.5, Sonnet 5.5 and Haiku 5.5 each scored 99% on its political-evenhandedness evaluation. That is Anthropic’s own test, and no equivalent figure appears for Sol or Argon in our sources, so we do not treat it as a differentiator.
Six Real-World Examples
Vendor demos prove little, so we collected documented uses with a named organization or a dated source. The list is short because public production data on models this new is thin. Each example notes who reported it, so you can weigh the claim yourself.
- Asana and GPT-6.1 Sol. OpenAI reported on October 9 that Asana’s optimized browser-agent workflow averaged about $0.47 per run and roughly four minutes. Asana ran the study and OpenAI published it. It is a customer test, not a general benchmark.
- Google engineers and Gemini 4 Argon. Google says its own engineers apply Argon to everyday debugging, large-scale codebase migrations and algorithm design, and that it posted 77.9% on DeepSWE v1.1.
- Google’s quantum research team. Google says Argon helped its researchers cut performance bottlenecks in quantum algorithms, and in one case reached a result 40% better than existing public literature within minutes. This is Google’s description of internal work.
- Wiz and Argon in penetration testing. On Wiz’s internal black-box penetration test, which measures how a model analyzes a live network system without source code, Google reports Argon beat Gemini 3.8 Flash Cyber on attack surface probing, vulnerability identification and proof-of-concept generation.
- Five Argon builders. On October 23, Google highlighted five builders using Argon for coding, mathematics, interactive tools and 3D-world projects.
On the distribution side, AWS announced general availability of GPT-6.1 Sol on Amazon Bedrock in September, and the AWS availability notice sits on the public record. One report also places the model in Microsoft Foundry alongside GPT-6 Astra, Sol and Luna.
Notice the pattern. The examples with real numbers are mostly vendor-reported, and only Asana is a true third-party customer. Treat all of them as directional. The strongest evidence for any model remains the test you run on your own data.
Plans and Access: ChatGPT, Claude and Gemini
Many teams meet these models through a chat product long before they touch an API. Access rules differ sharply, and they can decide the winner for non-developers. We cover the confirmed plan facts here and flag where prices were not available in our sources. For broader context on the field, our AI and machine learning coverage tracks each vendor’s lineup.
| Vendor | Where the model appears | Plans with access | Plans without |
|---|---|---|---|
| OpenAI | ChatGPT Work and Codex, OpenAI API | Plus, Pro, Business, Enterprise, Edu | Free and Go (these use GPT-6 Luna) |
| Anthropic | Claude plans, Claude Platform API | Max 5x, Max 20x, Team confirmed in the Oct. 7 update | Full plan list not confirmed in our sources |
| Gemini app, AI Mode in Search, Google AI Studio, Gemini API, Antigravity | Google AI Pro and Ultra in the app, Ultra in Antigravity | Lower tiers, depending on product |
OpenAI’s help pages describe a GPT-6 Pro option powered by GPT-6 Astra for the $100 and $200 Pro plans, plus Business and Enterprise, according to pages dated October 24 and 25. That is a separate and more expensive route than Sol. OpenAI also lists Free, Go, Plus, Pro, Business, Enterprise and Edu tiers, with the GPT-6 rollout still limited to eligible user groups as of this week.
Google lists Google AI Plus, Pro and Ultra, with multiple Ultra tiers. We could not confirm current subscription prices from Google’s pages, so we omit them. The practical point stands without prices. Argon in the Gemini app requires a paid Pro or Ultra plan, and Antigravity access is tied to Ultra. A free Google account will not give you the new model.
Pros and Cons of Each Model
GPT-6.1 Sol
- Pro: OpenAI’s claim of near-Astra capability at one-fifth of Astra’s price, if it holds on your tasks, is the best value proposition in the trio.
- Pro: Documented Batch and Flex discounts (50% off), US and EU data residency, and an Ultrafast tier give finance and compliance teams levers to pull.
- Pro: Wide distribution across ChatGPT Work, Codex, the API and Amazon Bedrock.
- Con: Not available on Free or Go plans, and Enterprise and Edu owners must enable it.
- Con: Fast mode doubles the price, and regional processing adds 10%, so real bills can exceed the $2 and $10 headline.
Claude Sonnet 5.5
- Pro: A fresh cache-read cut to $0.10 on October 7 makes long agent sessions cheaper, and Anthropic puts the saving near 20% for most agentic work.
- Pro: Clear lineup logic, with Opus 5.5 above, Haiku 5.5 below and one family design.
- Pro: Max and Team plans bundle monthly Claude Platform API credits ($100, $200 and up to $500 pooled), which helps teams that already pay for seats.
- Con: Cache writes cost $2.50 per million tokens, above the $2 base input price, which penalizes workloads that rewrite their prompts often.
- Con: Few absolute benchmark numbers in our sources, only relative gains over Sonnet 5.
Gemini 4 Argon
- Pro: The only trio member with published absolute scores for coding, video and vulnerability remediation, plus a 1M-token output limit.
- Pro: Same $2 and $10 price as rivals through November 30, with a 95% cache discount.
- Pro: Deep integration across Search, the Gemini app, AI Studio and Antigravity.
- Con: The price doubles to $4 and $20 on December 1, making it the costliest of the three from then on.
- Con: Consumer access sits behind paid Pro and Ultra plans, and the strongest cyber features sit behind Fairwind.
Use-Case Recommendations
We turn the evidence into six concrete recommendations. Each names a default, a reason and a condition that would flip the choice. These are judgments, not vendor claims, and the right answer for your team depends on a test you run yourself.
- Long-running coding agents with heavy prompt reuse. Start with Claude Sonnet 5.5 or GPT-6.1 Sol. Both charge $0.10 for cache reads and have no announced change past November 30. Flip to Argon only if your own tests show it finishes tasks in clearly fewer output tokens.
- High-volume support and classification. Pick the cheapest tier that passes quality checks, which may be a smaller model entirely. To see how small models compare, read our DeepSeek, Haiku 5.5 and Gemini Flash breakdown. Among the three here, Sol’s Batch discount helps overnight jobs.
- Long-form generation and large document rewrites. Test Argon first for the 1M-token output limit, and budget for the December price. At $20 per million output tokens, a 500,000-token response costs $10.
- Video and screen-heavy analysis. Argon, because it carries the only video benchmark (91.7% on LVBench) in this group.
- Security remediation and vulnerability triage. Argon for its 68% CWE-bench v1 first-place tie, then apply to Fairwind if you are a trusted defender. Anthropic’s Mythos 5.1 offers a vetted alternative for qualifying teams.
- EU-bound regulated workloads. Sol, which lists US and EU data residency, at a 10% regional premium where available. Confirm residency terms with your vendor contact before launch.
A seventh case deserves mention: mixed fleets. Many teams now route easy requests to a small model and hard ones to a mid-tier model like these. That routing often saves more money than any choice between the three, because cheap models handle the majority of traffic. Build the router first and the vendor choice becomes reversible.
Migration Guide: Switching Models Without Breaking Production
Moving between these models takes days when you prepare and weeks when you do not. The steps below work for any direction of travel, from Sol to Sonnet, from Sonnet to Argon or from an older generation to any of the three. Plan one full sprint for a production system with tool calls.
- Freeze a golden set. Collect 50 to 200 real requests with known good outputs. Include your worst edge cases and long contexts.
- Put the model name behind config. Never hard-code an ID. The IDs we confirmed are
gpt-6.1-solandgemini-4-argon. Copy the Claude ID from Anthropic’s model list rather than guessing it. - Replay and score. Send the golden set to each candidate with identical tool definitions, and record pass rate, output tokens and latency.
- Compute cost per passed task. Divide total spend by successes. This number, not the per-token rate, is the one to compare.
- Check caching behavior. Each vendor handles cache writes and reads differently. Anthropic charges $2.50 per million for writes, so measure real hit rates before you trust the $0.10 headline.
- Shadow-run for a week. Mirror 5% to 10% of live traffic to the new model and compare outputs without exposing users.
- Ramp gradually. Move 10%, then 50%, then 100%, with a one-line rollback to the old ID.
Setup details differ by vendor, and we have step-by-step guides for two of the three. Our Claude Sonnet 5.5 API setup walkthrough covers keys, requests and cost tracking, and the Claude Agent SDK guide covers agent loops. For OpenAI, use the GPT-6.1 Sol guide linked earlier.
The harness below replays a golden set and computes cost per passed task from the verified rates. It is deliberately provider-neutral. You supply a call_model function for each vendor’s SDK, so the logic stays the same when you swap models.
import json, time
# List prices per 1M tokens as of Oct 25, 2026 (USD)
PRICES = {
"gpt-6.1-sol": {"in": 2.0, "out": 10.0, "cache_read": 0.10},
"claude-sonnet": {"in": 2.0, "out": 10.0, "cache_read": 0.10, "cache_write": 2.50},
"gemini-4-argon": {"in": 2.0, "out": 10.0, "cache_read": 0.10}, # $4 / $20 from Dec 1
}
def cost(model, usage):
p = PRICES[model]
return (
usage.get("input", 0) * p["in"]
+ usage.get("output", 0) * p["out"]
+ usage.get("cache_read", 0) * p["cache_read"]
+ usage.get("cache_write", 0) * p.get("cache_write", p["in"])
) / 1_000_000
def replay(model, golden, call_model, grade):
spent, passed, latencies = 0.0, 0, []
for case in golden:
t0 = time.time()
answer, usage = call_model(model, case["prompt"]) # your SDK wrapper
latencies.append(time.time() - t0)
spent += cost(model, usage)
passed += 1 if grade(case, answer) else 0
latencies.sort()
return {
"model": model,
"pass_rate": passed / len(golden),
"total_cost": round(spent, 4),
"cost_per_pass": round(spent / max(passed, 1), 4),
"p50_latency_s": latencies[len(latencies) // 2],
}
if __name__ == "__main__":
golden = json.load(open("golden.json"))
# results = [replay(m, golden, call_model, grade) for m in PRICES]
Before December 1, add a second price table to the script with Argon at $4 and $20 and rerun the numbers. A model that wins today may lose in five weeks. If the gap flips your decision, you want to know before the contract renews, not after the invoice arrives.
Verdict: Which Model to Pick
Here is the clear call, with the data behind it. On price alone, GPT-6.1 Sol and Claude Sonnet 5.5 tie at $2 and $10 with $0.10 cache reads and no announced change. Gemini 4 Argon ties them today and then costs exactly twice as much from December 1. For any workload you will run into 2027, that makes Sol and Sonnet 5.5 the lower-risk budget choices and Argon the one that needs a performance justification.
Argon earns that justification in three places where it holds published evidence the others lack: 77.9% on DeepSWE v1.1, 91.7% on LVBench and a first-place tie at 68% on CWE-bench v1, plus the 1M-token output limit. If your work is video analysis, security remediation or very long generation, Argon is the model to test first, even at the higher price. For general chat and coding at scale, the evidence does not yet justify paying double.
Between Sol and Sonnet 5.5, our sources show no verified winner. Sol brings the Batch discount, data residency and the widest cloud distribution. Sonnet brings the cleaner family structure, a fresh cache-price cut and a strong cost-per-task story against its own predecessor. Our recommendation: pick Sol if you run batch or EU-bound work, pick Sonnet 5.5 if your workload is cache-heavy agent loops, and run your own golden set before you commit either way.
The takeaway: this AI model comparison ends in a tie on sticker price and a split on capability. The cheapest decision is to build a router, keep two vendors warm, and let measured cost per passed task choose the default. Revisit the numbers on December 1, and again when Anthropic, OpenAI or Google ships its next update, because the last month showed how fast these tables go stale.
FAQ
Which is cheaper, GPT-6.1 Sol, Claude Sonnet 5.5 or Gemini 4 Argon?
Today all three list $2 per million input tokens and $10 per million output tokens, with $0.10 cached input. From December 1, 2026, Gemini 4 Argon moves to $4 and $20, so it becomes the most expensive of the three. Sol and Sonnet 5.5 have no announced change.
When did each model launch?
GPT-6.1 Sol and Claude Sonnet 5.5 both launched in late September 2026, and sources differ on exact days (September 22 or 29 for Sol, September 27 or 28 for Sonnet). Google announced Gemini 4 Argon on September 30 and made it globally available on October 13.
Is Gemini 4 Argon better at coding than GPT-6.1 Sol?
We cannot say. Google reports 77.9% for Argon on DeepSWE v1.1. OpenAI says Sol matches GPT-6 Astra on the same test but gives no absolute score we could verify. Without matched settings, a direct ranking would be a guess.
Can free ChatGPT or Gemini users access these models?
Not the new ones. GPT-6.1 Sol is not on the Free or Go plans, which use GPT-6 Luna. Gemini 4 Argon in the Gemini app requires Google AI Pro or Ultra. Developers can call both through their APIs.
What changed for Claude Sonnet 5.5 pricing in October?
On October 7, Anthropic cut cache-read pricing from $0.20 to $0.10 per million tokens and said the move lowers the cost of most agentic workloads by about 20%. Base prices stayed at $2 input and $10 output.
Are these the most powerful models from each company?
No. GPT-6 Astra sits above Sol, and Claude Opus 5.5 sits above Sonnet 5.5. Gemini 4 Argon is the top of Google’s published lineup in our sources. Sol and Sonnet 5.5 are the value tier for OpenAI and Anthropic.
Which model should I choose for batch jobs?
GPT-6.1 Sol has the only batch discount we could verify, with Batch and Flex pricing 50% below Standard. Check Anthropic’s and Google’s current documentation for their own batch terms before deciding.
How should I test these models before switching?
Build a golden set of 50 to 200 real requests, replay it through each model with identical tools, and compare cost per passed task. Shadow-run live traffic for a week before moving users over.
