There is no proven overall winner. Google’s September 30, 2026 announcement introduced Gemini 4 Argon with a phased rollout, and Google’s comparison table shows different models performing well on different evaluations. Choose among Argon, GPT-6 Astra, Claude Fable 5.1, and Claude Opus 5.5 by the work you need done, what you can access, the full cost, and how each performs on a small set of your own representative tasks.
What Gemini 4 Argon is—and who can use it
Google describes Gemini 4 Argon as a model for complex software engineering, enterprise knowledge work such as legal and finance tasks, and cybersecurity defense. Those are Google’s stated intended uses, not independent findings that Argon is best for each workload.
In its September 30, 2026 launch announcement, Google said initial access was rolling out to a set of trusted cyber defenders through the Fairwind Program. The company described broader access as a later, phased expansion, starting with paid API customers and Google AI Ultra subscribers. The announcement gave no firm date for that expansion, so check availability for your account and region rather than assuming you can use Argon now.
Google also reported a 1 million-token output limit, up from the previous 64K limit. That is an output limit; it should not be mistaken for a statement that Argon accepts a 1 million-token input or has a 1 million-token context window. Google’s table separately reports an Argon result on a GraphWalks evaluation for a 256K-to-1M context subset.
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What Google’s comparison says—and what it cannot settle
Google’s published table compares Argon with GPT-6 Astra, Claude Fable 5.1, and Claude Opus 5.5 across knowledge work, agentic coding, science and math, long context, computer use, multimodal understanding, and cybersecurity. The selected results below are Google-reported figures, not scores from an independently run, controlled head-to-head test.
| Evaluation | Reported result | What it can tell you |
|---|---|---|
| Vals Index | Gemini 4 Argon: 68.9% | A result on one knowledge-work evaluation; it does not establish performance across every kind of business or research task. |
| DeepSWE v1.1 | Gemini 4 Argon: 77.9% | A result on one software-engineering evaluation, not a guarantee of success on your codebase. |
| GraphWalks, 256K-to-1M context subset | Gemini 4 Argon: 84.2% | A result on a long-context subset. It does not show that every long-document task will work equally well. |
| LVBench | Gemini 4 Argon: 91.7% | A result on one video-understanding evaluation, not a broad measure of all visual or video work. |
| FrontierSWE v2 | GPT-6 Astra: 65.5%, the highest result in Google’s table | A coding result on this particular evaluation. |
| Terminal-Bench Science 0.1 | GPT-6 Astra: 68.1%, the highest result in Google’s table | A result on this particular science evaluation. |
| OSWorld-2.0 | GPT-6 Astra: 72.6%, the highest result in Google’s table | A result on this particular computer-use evaluation. |
| Terminal-bench 4.0 | Claude Opus 5.5: 66.4%, the highest result in Google’s table | A result on this particular evaluation; the name alone does not establish performance in every terminal workflow. |
| PostTrainBench | Claude Opus 5.5: 49.3%, the highest result in Google’s table | A result on this particular evaluation, not a general-purpose quality score. |
Google says Argon’s results are generally pass@1, run through the Gemini API at its highest thinking settings. For other models, the table generally uses provider-reported values at maximum thinking or reasoning settings unless indicated otherwise. Its methodology draws on a mix of self-computed tests, public leaderboards, provider system cards, and different evaluation harnesses or settings. Some values are missing, and some comparisons do not use identical data or setups. The scores therefore are not a single, controlled ranking of all four models.
Use each result as a clue about a particular test, not a forecast of how the model will perform on your work. Google’s launch post also describes internal workflow examples, including large code migrations; those are vendor-reported examples, not independent verification.
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Choose by the job, not by a model’s headline score
Long-horizon coding and software engineering
Argon’s announced focus includes complex software engineering, and Google reports a 77.9% result on DeepSWE v1.1. But GPT-6 Astra has the highest result in Google’s table on FrontierSWE v2, at 65.5%. Those are different evaluations, so the two figures do not decide which model will handle your repository better. Test the models you can access on representative issues, including the repository context, tools, and review process you actually use.
Enterprise research, drafting, and long documents
Google positions Argon for enterprise knowledge work such as legal and finance work. Its table reports 68.9% on Vals Index and 84.2% on the GraphWalks 256K-to-1M context subset. Neither score establishes accuracy for a particular contract, financial analysis, or document collection. For consequential work, compare outputs against known answers and have a qualified person review them.
Visual and video analysis
Google reports 91.7% for Argon on LVBench. Treat that as a result on one video benchmark, not evidence that Argon will be the best choice for every image, clip, or production workflow. Try examples resembling your real inputs and judge whether the model identifies the details you need.
Rank #3
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- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
- AMD RADEON 8090S iGPU GAMING PC - The AMD Radeon RX 8060S offers all 40 CUs with up to 2.9 GHz graphics clock and uses the new RDNA 3.5 architecture. The powerful iGPU is positioned between an RTX 4060 and 4070 laptop GPU and therefore enables gaming in FHD at maximum details in most demanding games. The 8060S can also utilize the full 128GB pool, which is perfect for running LLMs such as Deepseek 70B Q8, which runs comfortably on this machine.
- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 12% better performance in digital content workloads.
- QUAD SCREEN 8K DISPLAY SUPPORT - EVO-X2 AI Mini PC support 4-screen 4K/8K output via HDMI 2.1 (8K@60Hz), DisplayPort 1.4 (4K@60Hz), and dual USB 4 40Gbps Transfer speed (supporting PD3.0/DP1.4/DATA). Ideal for gaming, video editing, and multitasking, it provides expansive and crisp multi-display support.
Computer use and science
In Google’s table, GPT-6 Astra has the highest reported results on OSWorld-2.0 (72.6%) and Terminal-Bench Science 0.1 (68.1%). If computer interaction or science work is central to your use case, those results make GPT-6 Astra worth including in a trial, but they do not establish a general advantage outside those evaluations.
Cybersecurity defense
Cyber defense is among Argon’s stated target uses, but Google’s announcement described initial access through the Fairwind Program for trusted cyber defenders. That makes eligibility a practical first question. For defensive security work, evaluate models only in an authorized environment and keep appropriate human review and safeguards in place.
Compare access and cost before committing
At launch, Google announced introductory Gemini API rates of $2 per million input tokens and $10 per million output tokens. It said cached input tokens would receive a 95% discount from the input rate. After the introductory period, Google said the rates would be $4 per million input tokens and $20 per million output tokens. The announcement did not say when the introductory period ends; confirm current pricing before estimating spend. These are Google-announced Argon API rates, not a comparison with Claude or GPT prices.
When estimating an API workload, account for input tokens, generated output, and any eligible cached input separately. A long prompt or repeated generation can make output usage materially different from input usage. Compare the expected total against current provider pricing; current Claude and GPT prices and access terms are not established here.
Run a small evaluation on your own tasks
A focused trial is more useful than trying to declare a universal winner from unlike benchmark results. Use the same representative tasks for each model you can access, and decide in advance what a satisfactory answer looks like.
- Select real examples. Pick a few tasks from the work you care about, such as a code change, a long-document question, a video summary, or an authorized computer-use workflow.
- Keep the inputs and success criteria consistent. Give each available model the same relevant context and ask for the same outcome. Decide what counts as correct, complete, and usable before comparing responses.
- Check the work, not just the presentation. Verify factual claims against trusted material, run or review generated code, and inspect actions taken in any computer-use task.
- Record practical trade-offs. Note whether the model was available to your account, how much review the result required, how long the task took, and the usage cost at current rates.
- Choose for the consequences of error. For legal, finance, code, and security work, make human review appropriate to the risk part of the workflow rather than treating a fluent answer as verified.
For high-stakes work, a model that performs well on a benchmark can still make errors that matter. Google says Argon’s safety controls were being strengthened before broader access and describes the model as intentionally capable in cyber defense. Keep access controls and review procedures suited to the work, especially when outputs could affect systems, finances, or legal decisions.
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