Three flagship models landed within about ten days of each other: Claude Opus 5.5 on September 22, GPT-6.1 Sol on September 29, and Gemini 4 Argon on September 30. If you build AI products, that probably means a pile of messages asking "should we switch?"
Short answer: probably not, at least not before you test on your own workload. This guide gives you the verified numbers (pricing, context, vendor benchmarks), what they do and don't tell you, and a simple method to choose.
All prices below were checked on October 19, 2026. Introductory prices change quickly, so confirm them on the provider's pricing page before you commit.
The three contenders at a glance
Gemini 4 Argon (Google)
Google announced Argon on September 30 as a frontier model for real-world coding, enterprise knowledge work and cyber defense. It started with a small group of trusted defenders through Google's Fairwind Program, and access has been widening since. Two things stand out:
- Output limit of 1M tokens, up from 64K on the previous generation. That matters for long agent runs and big code migrations.
- Introductory pricing of $2 per million input tokens and $10 per million output tokens, with cached input at 95% off. Google's own footnote says this rises to $4 and $20 later, and third-party trackers put the change at December 1, 2026.
GPT-6 family (OpenAI)
OpenAI split GPT-6 into tiers: Astra (the top model), Sol (balanced) and Luna (cheap and fast). Sol is priced at $2 input and $10 output per million tokens, and Luna at $0.10 and $0.50. A newer GPT-6.1 Sol followed on September 29. OpenAI also says cached input reads get a 90% discount.
Claude (Anthropic)
The current lineup is Claude Fable 5.1, Opus 5.5, Sonnet 5.5 and Haiku 5.5. Third-party trackers list Opus 5.5 at $4 / $20, Sonnet 5.5 at $2 / $10 and Fable 5.1 at $10 / $50 per million tokens, with a roughly 1M-token context window across current models. Anthropic's own pricing page is the final word, so check it before budgeting.
Specs and pricing comparison
| Gemini 4 Argon | GPT-6.1 Sol | GPT-6 Luna | Claude Opus 5.5 | Claude Sonnet 5.5 | |
|---|---|---|---|---|---|
| Input / 1M tokens | $2 (then $4) | $2 | $0.10 | $4 | $2 |
| Output / 1M tokens | $10 (then $20) | $10 | $0.50 | $20 | $10 |
| Context window | ~1M (1,048,576) | ~1.05M | ~1.05M | ~1M | ~1M |
| Standout | 1M output limit, long agent runs | Strong cost efficiency | Very cheap tier | Strong coding and agent work | Mid-price option |
| Watch out for | Price doubles after intro period | Input price rises above ~272K tokens | Smaller model, not for hard tasks | Highest list price of the group (excluding Fable) | Check current limits |
Sources for pricing and context: provider announcements plus third-party trackers (linked at the end). Claude prices come from trackers, so verify them on Anthropic's site.
What the benchmarks actually say
Each lab publishes charts where its own model looks great. That is normal, and it is also why you should read them carefully.
On DeepSWE v1.1, a long-horizon software engineering benchmark:
- Google reports 77.9% for Gemini 4 Argon.
- OpenAI reports 68.8% for GPT-6 Sol at max effort, and cites 69.9% for Claude Fable 5 at its highest setting.
On the surface, Argon leads. But these numbers come from different labs, at different times, with different effort settings and tooling. They are useful for a rough ranking, not for a final decision. OpenAI also makes the point that Sol reaches similar scores at much lower cost per task, which is a reminder that the score and the bill are two separate columns.
Argon also reports 68% on CWE-bench v1 for fixing security vulnerabilities, tied for first, and Google highlights its cyber-defense focus. If your work is security-heavy, that is worth testing. Just note that the most cyber-capable version is being released to trusted defenders first.
What it really costs: a simple worked example
Take a typical request with 2,000 input tokens and 500 output tokens. Here is the token cost at list price.
| Model | Cost per request | Cost for 100,000 requests |
|---|---|---|
| GPT-6 Luna | $0.00045 | $45 |
| Gemini 4 Argon (intro price) | $0.009 | $900 |
| GPT-6.1 Sol | $0.009 | $900 |
| Claude Sonnet 5.5 | $0.009 | $900 |
| Gemini 4 Argon (after price rise) | $0.018 | $1,800 |
| Claude Opus 5.5 | $0.018 | $1,800 |
| Claude Fable 5.1 | $0.045 | $4,500 |
Three things people forget:
- Argon's price roughly doubles after the intro period. If you design your system around the launch price, rerun the numbers at $4 / $20.
- Retries cost money. A cheaper model that needs three attempts can cost more than a pricier model that gets it right once. Measure cost per successful task.
- Reasoning tokens are billed as output. A model that "thinks" a lot can cost far more than the per-token price suggests.
Which model for which job?
This is how I would approach it for real client work, based on what each vendor says it is built for. Treat it as a starting hypothesis, then test.
Coding assistants and agents. Start with Argon, Sol and Opus 5.5 side by side on your own repository. They are all strong here. The winner depends on your language, your codebase size and how much hand-holding the model needs.
RAG and document Q&A. The model matters less than people think. Chunking, retrieval quality and reranking usually decide the answer quality. If you have not read it yet, our post on RAG vs OKF covers where retrieval-only systems break. For the generation step, a mid-priced model is usually enough, and a cheap model like Luna is worth testing for simple lookups.
Business automation and tool calling. Here, reliability beats raw intelligence. You want correct tool selection, valid arguments and clean recovery from failures. If you are connecting models to your own systems, read our MCP developer guide first.
Very long documents or huge codebases. All three advertise roughly 1M tokens of context. Check the fine print: Sol's price rises above about 272K input tokens, Argon shares its window between input, thinking and output, and long-context accuracy varies by task. Test with your real documents, not a synthetic needle-in-a-haystack run.
High volume, low complexity (classification, extraction, routing). Start with the cheapest tier. At 100,000 requests the gap between Luna and a flagship is roughly $45 versus $900 or more.
Anything with access to live systems. Whatever model you pick, permissions and prompt-injection defenses matter more than the model name. See AI Agent Security in 2026 for the checklist we use.
How to run your own comparison in one afternoon
You do not need a big framework. You need honest test cases.
- Collect 20 to 30 real tasks from your own product: real support questions, real bugs, real documents. Include a few cases where the correct answer is "I don't know."
- Fix the setup. Same prompt, same tools, same retrieved context, same temperature for every model.
- Define "pass" before you run anything. For code, that means your test suite passes. For RAG, it means the answer is correct, grounded in the retrieved text and cites the right source.
- Run each task at least three times, because model output varies.
- Log four things per run: pass or fail, latency, tokens used, and cost.
- Calculate cost per successful task, not cost per token.
- Have a human review a sample of the outputs. Do not let another model be the only judge.
- Repeat the test when the price or model version changes. Argon's pricing change in December is a good reminder.
If you build agents, also try a routing setup: a cheap model for easy requests and a stronger one for hard cases. It often cuts cost a lot, but compare it against a single-model baseline, because routing adds its own failure modes.
Frequently asked questions
Is Gemini 4 Argon better than GPT-6?
On Google's own reported coding benchmark it scores higher than OpenAI's reported numbers for Sol. But those figures come from different labs and settings, and Sol is built to deliver strong results at a lower cost per task. "Better" depends on your workload, so test both on your tasks.
How much does Gemini 4 Argon cost?
During the introductory period it costs $2 per million input tokens and $10 per million output tokens, with cached input at 95% off. Google states the price rises to $4 and $20 afterwards, and trackers point to December 1, 2026. Confirm on Google's pricing page.
Is Claude good for coding?
Claude models are widely used for coding and agent workflows, and Opus 5.5 and Sonnet 5.5 are the ones most developers compare against Argon and Sol. As with any model, run it on your own repository before deciding.
Which model is cheapest for production?
For simple, high-volume tasks, GPT-6 Luna has the lowest list price in this comparison at $0.10 input and $0.50 output per million tokens. For harder tasks, compare cost per successful task, since cheaper models can need more retries.
Do I need the largest context window?
Not usually. Most applications do better with good retrieval and a focused prompt than with a million tokens of raw context, which is slower and more expensive.
Can I use more than one model in the same app?
Yes. Many production systems route simple requests to a cheaper model and escalate hard ones. Keep the routing rules simple, log every decision and compare against a single-model baseline.
Final thoughts
There is no universal winner, and anyone who tells you otherwise is selling something. Argon looks strong on long agent runs and coding, Sol is aggressive on cost efficiency, and Claude's Opus and Sonnet tiers are serious options for coding and tool-heavy work. What matters is how each one performs on your tasks at your volume.
Pick two or three candidates, run the afternoon test above, and let cost per successful task make the decision. Then check again in a few months, because this market changes faster than any comparison article, including this one.
Disclosure: Prices, limits and availability were accurate on October 19, 2026 and may change. Benchmark scores are reported by the model vendors and have not been independently reproduced by BuildWithJIT.