Gemini 4 Argon vs GPT-6 Astra vs Claude Opus 5.5: Benchmarks

Gemini 4 Argon led most rows in Google's September 30 launch comparison, with several exceptions. In the 19-row table Google published with the launch on September 30, 2026, Argon has the top score among itself, GPT-6 Astra and Claude Opus 5.5 on 13 rows, ties on one, and loses five: three to Astra and two to Opus 5.5^[1]. The losses cluster around terminal work, harder software benchmarks and computer use, which is exactly where many developers will judge it.

This comparison puts every number from Google's table side by side, adds the documented request limits, and explains the caveats in Google's methodology. Search interest after launch asked the same things: "gemini 4 argon benchmarks", "gemini 4 argon performance relative to other models", "gemini 4 vs gpt 6 astra", "gemini 4 vs claude". Google now documents Gemini 4 Argon access through the Gemini API and Google AI Studio^[2]. The comparisons below preserve the dated launch results; they are not a new independent evaluation or a claim about today's leaderboard.

TL;DR

• Overall: among the three models, Gemini 4 Argon has the best score on 13 of 19 rows in Google's table, GPT-6 Astra on 3, Claude Opus 5.5 on 2, with one tie^[1].
• Argon's clearest leads: Harvey's Legal Agent Benchmark (19.6% vs 5.4% and 3.8%), long-context GraphWalks 256K–1M (84.2% vs 71.8% and 66.8%) and LVBench long video (91.7%)^[1].
• Where Argon trails: Opus 5.5 leads Terminal-bench 4.0 (66.4% vs 57.4%) and PostTrainBench; Astra leads FrontierSWE v2, Terminal-Bench Science and OSWorld-2.0^[1].
• Context and output: Argon shares 1,048,576 tokens across input, thinking and output. Native requests stop at 32,768 tokens for JSON or 65,536 for streaming; long decode continues across requests^[3].
• Cost: compare current rates and total task usage, including thinking and continuation. The Gemini 4 Argon pricing guide explains the billing dimensions.

Gemini 4 Argon benchmarks: the full table

These are the numbers from the benchmark table in Google's announcement. Google's table also includes Claude Fable 5.1, shown here in its own column so the "vs Fable" question has an answer too. The best score among Argon, Astra and Opus 5.5 is in bold; a dash means Google reported no result^[1].

Head to head with Claude Opus 5.5 alone, Gemini 4 Argon is ahead on 14 of the 18 rows where both have a score and behind on four: FrontierSWE v2, Terminal-bench 4.0, PostTrainBench and Terminal-Bench Science^[1].

Read the table with Google's methodology in hand

Every score in that table comes from Google's announcement, and Google's methodology page explains where each number came from. Three details change how much weight the gaps deserve^[4]:

1. Most rival scores are self-reported. Google says results for non-Gemini models are "sourced from providers' self reported numbers unless otherwise mentioned", using each rival's maximum available reasoning setting where reported.
2. Some Argon scores are self-computed. DeepSWE v1.1 and Terminal-bench 4.0 results for Argon were run by Google, while Astra, Fable and Opus figures come from public leaderboards or system cards. That is normal practice, but it means the harness is not always identical.
3. Inputs were not always equal. On LVBench, Google used 1 frame per second for Gemini but 800 frames for Astra, 600 for Opus 5.5 and 300 for Fable 5.1 "due to API limitations". The models did not see the same frame budget, so treat the long-video gap with some care.

The table is useful for choosing what to test. It does not supply confidence intervals or a common test harness for every row, so a small lead should not be treated as a guaranteed difference on your workload. Larger gaps can justify a focused evaluation without proving that the same ranking will hold in your application.

Context and output change the integration decision

The earlier version of this comparison described Argon's context window as unpublished and its output as one million tokens in a single response. Google's current developer guides now make the limits more specific^[2][3].

Sources: Google's current model migration and thinking guides^[2][3].

Long decode is useful headroom for large tasks, but it is not a guarantee that one HTTP response contains the finished report. reAPI does not automatically continue on the server. Your client must handle continuation explicitly, account for each request and preserve the information required to resume.

The launch comparison used Astra and Opus output specifications from their own documentation. Those historical limits should not be combined with Argon's long-decode limit as if every figure described the same single-request operation. Check the current GPT-6 Astra and Claude Opus 5.5 pages when deciding how much output your integration needs. Current prices belong on those model pages as well.

Gemini 4 Argon vs GPT-6 Astra

Argon wins most of the knowledge-work and long-context rows against Astra, and by wide margins on the legal and finance agent tests: 19.6% vs 5.4% on Harvey's benchmark and 65.4% vs 53.5% on Vals Finance Agent v2^[1]. Compare that task advantage with the token usage and current rates of your own evaluation before making a cost claim.

Astra is worth testing first when your workload resembles the computer-use and software benchmarks it leads in this table. It leads OSWorld-2.0 (72.6% vs 69.2%), FrontierSWE v2 (65.5% vs 55.0%) and Terminal-Bench Science (68.1% vs 57.6%)^[1]. The 10.5-point gaps on FrontierSWE v2 and Terminal-Bench Science are the largest losses Argon takes in its own launch table, so teams whose work looks like those benchmarks should test before switching. Astra also edges Argon on Terminal-bench 4.0, 58.2% to 57.4%.

Gemini 4 Argon vs Claude Opus 5.5

This is the closest pairing on coding. Argon edges Opus 5.5 on DeepSWE v1.1 (77.9% vs 74.2%) and Vibe Code Bench (91.9% vs 90.3%), while Opus 5.5 wins Terminal-bench 4.0 by 9 points (66.4% vs 57.4%) and PostTrainBench by 4 (49.3% vs 45.3%)^[1]. If your agents live in a shell, Opus 5.5 holds up well against the newer model.

Outside coding, Argon pulls away. The legal benchmark gap is 15.8 points, long-context GraphWalks at 256K to 1M is 17.4 points, and LABBench 2 is 15.7 points^[1]. For a production decision, run the same source material and acceptance checks on both. A benchmark lead does not determine how many tokens either model will need to finish your task.

Which model to test for each job

• Legal and finance research, long document sets and video: include Gemini 4 Argon in your evaluation because these are among its largest leads in Google's launch table. Check source accuracy and completeness on your own material.
• Long migrations or reports: evaluate Gemini 4 Argon's long decode with a client that handles continuation. Reserve room for thinking as well as the answer; a shared context window is not an independent output allowance.
• Terminal-heavy coding agents: start with Claude Opus 5.5 alongside Argon. Opus leads Terminal-bench 4.0 in this comparison, but the difference still needs checking against your tools and tasks.
• Computer use and difficult software tasks: include GPT-6 Astra, which leads OSWorld-2.0 and FrontierSWE v2 in the dated table.

Keep the comparison reproducible. Use the same prompts and source material, record each model's reasoning setting and budget, and distinguish an unfinished response from a completed but incorrect answer. For tool use, verify the full round trip rather than stopping when the model returns a tool call.

On reAPI, the Gemini 4 Argon model page links to current pricing and the request reference. The comparison no longer asks you to wait for Google to publish an API; it asks which model finishes your workload under the conditions you can afford.

FAQ

Is Gemini 4 Argon better than GPT-6 Astra?

On most of Google's table, yes: Argon has the higher score on 14 of 19 rows against Astra, with one tie. Astra leads on FrontierSWE v2, Terminal-Bench Science, OSWorld-2.0 and, narrowly, Terminal-bench 4.0^[1].

Is Gemini 4 Argon better than Claude Opus 5.5?

On 14 of the 18 comparable rows in Google's table. Opus 5.5 leads on Terminal-bench 4.0, PostTrainBench, FrontierSWE v2 and Terminal-Bench Science^[1].

How does Gemini 4 Argon compare with Claude Fable 5.1?

Argon scores higher than Fable 5.1 on 15 of the 17 rows where Google reports both. Fable 5.1 is slightly ahead on FrontierSWE v2 (56.3% vs 55.0%) and Terminal-bench 4.0 (57.9% vs 57.4%)^[1].

What is Gemini 4 Argon's best benchmark result?

GraphWalks up to 128K at 99.7%. The most meaningful wins are the large gaps: Harvey's Legal Agent Benchmark at 19.6% against 5.4% for Astra and 3.8% for Opus 5.5, and GraphWalks 256K to 1M at 84.2%^[1].

Are these benchmark scores independent?

Not fully. They come from Google's announcement; Google says most rival scores are self-reported by each provider, and it ran some Argon tests itself^[4].

Which is cheapest: Gemini 4 Argon, GPT-6 Astra or Claude Opus 5.5?

Check current rates on the model pages and compare the cost of an accepted result. Input size, cache hits, reasoning and continuation can change the bill. The pricing guide explains how to build that comparison without relying on an old static price table.

Choosing between Argon, Astra and Opus 5.5

Google's September table gives Gemini 4 Argon a reason to be in an evaluation for knowledge work and long-context tasks. It also gives clear reasons to keep Opus 5.5 and Astra in the test set for terminal and computer-use workloads. Those are useful hypotheses from a dated, vendor-published comparison.

The decision should come from tasks your application can score: whether the change passes its tests, whether the report supports its claims, or whether the agent completes the requested action. Record the budget and continuation behavior alongside the result. That is how the launch comparison becomes a useful test of Gemini 4 Argon.

References

1. Google. Gemini 4 Argon: our next era of frontier intelligence (including benchmark table). Retrieved October 2026 from blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-4-argon

2. Google. What's new in Gemini 4 Argon. Retrieved October 14, 2026 from ai.google.dev/gemini-api/docs/generate-content/latest-model

3. Google. Gemini thinking. Retrieved October 14, 2026 from ai.google.dev/gemini-api/docs/generate-content/thinking

4. Google DeepMind. Gemini 4 Argon model evaluation: approach, methodology and results. Retrieved October 2026 from deepmind.google/models/evals-methodology/gemini-4-argon