Gemini 4 Argon vs GPT-6 Astra and Claude Opus: Benchmark Guide
Compare Argon, GPT-6 Astra and Claude Opus 5.5 across coding, terminal and long-context benchmarks, with methodology limits and a reproducible pilot plan.
Ofox 
Gemini 4 Argon does not lead every published coding benchmark. In Google’s comparison, Argon scores 77.9% on DeepSWE v1.1, above GPT-6 Astra and Claude Opus 5.5, but 55.0% on FrontierSWE v2, below both. Opus leads the same table’s Terminal-Bench 4.0 row. The useful question is which benchmark resembles your work and how each result was produced.
This is an analysis of published evidence checked October 14, 2026, not an Ofox hands-on shootout. We separate Google’s comparisons, an independent provider’s earlier evaluation, and a proposed evaluation worksheet. The worksheet is our practical contribution; its blank rows are not test results.
Coding results: read across tasks, not just down a leaderboard
| Benchmark | Gemini 4 Argon | GPT-6 Astra | Claude Opus 5.5 | How to interpret it |
|---|---|---|---|---|
| DeepSWE v1.1 | 77.9% | 74.1% | 74.2% | Argon leads this row; do not transfer that lead to all repository work |
| FrontierSWE v2 | 55.0% | 65.5% | 62.3% | Astra leads this row; task difficulty and evaluation setup matter |
| Terminal-Bench 4.0 | 57.4% | 58.2% | 66.4% | Opus leads this row; terminal-heavy work needs its own evaluation |
Sources: Google’s Argon model card and evaluation methodology. These are Google’s reported comparisons, not three models we ran under a single identical harness. For DeepSWE and Terminal-Bench, the methodology combines Google’s Argon evaluation with other providers’ public leaderboard or system-card results; reasoning settings and harnesses are relevant limitations. FrontierSWE values are attributed to the official leaderboard in Google’s methodology.
The differences are percentage points, not relative speedups. Argon is 3.8 points above Astra on DeepSWE, but 10.5 points below Astra on FrontierSWE. Neither subtraction predicts how many minutes a model will save on your repository. Avoid averaging these percentages into a new “overall coding score”: the test sets and tasks are not interchangeable.
Long-context and video scores answer different questions
| Published benchmark | Argon | Astra | Opus 5.5 | Boundary to preserve |
|---|---|---|---|---|
| GraphWalks, 256k–1M BFS F1 | 84.2% | 71.8% | 66.8% | Google’s self-computed evaluation of 200 problems; not proof of full-repository correctness |
| LVBench | 91.7% | 87.5% | 83.7% | Different video frame budgets; not an equal-input production cost comparison |
| AutomationBench | 51.3% | 41.4% | 42.5% | Private evaluation; distinct from Artificial Analysis’s similarly named benchmark |
The same methodology document gives Gemini video at 1 FPS and different frame limits for other models, including 800 frames for GPT and 600 for Opus. Therefore, the video row combines model behavior with differing input budgets. It supports investigating Argon for a video task, but does not establish a like-for-like accuracy or cost advantage for your clip length and sampling strategy.
Argon’s documented unified context is 1,048,576 input-plus-output tokens. That capacity makes a long-context pilot possible; it does not guarantee that every relevant fact in a large upload will be used correctly. For a repository task, test whether the proposed patch honors constraints scattered across files. For video, require timestamped evidence and compare against the actual frames. For research, require source-level support for the conclusion rather than merely accepting a fluent answer.
What the independent Artificial Analysis result adds
Artificial Analysis’s September 30 report evaluated Argon at high reasoning and reported an Intelligence Index of 53, tying GPT-6 Astra at max in that report. GPT-6.1 Sol at max scored 52. These settings are part of the result; “53 versus 53” is not proof of equal behavior at every effort level or latency budget.
The report’s Intelligence Index evaluation priced Argon at $1.99 per task during the launch discount, around 60% of Astra’s $3.26, but 2.7 times Sol’s cost. It estimated $3.98 for Argon at non-promotional pricing. Those are benchmark workload costs under the report’s assumptions, not universal prices for one user task. The report also describes substantially more output tokens from Argon—about 62,000 versus Astra’s 27,000 in that comparison—so input/output rates alone cannot rank completed-task costs.
Its factuality findings also require care: Argon’s reported hallucination rate was 15%, while accuracy was 50%, compared with Astra’s 63% accuracy. A lower hallucination rate cannot be restated as higher accuracy. Abstention, correct answers and incorrect answers need separate accounting. Consult the Artificial Analysis methodology before using its index as a procurement criterion.
The 78% AutomationBench-AA figure in that report is not the 51.3% AutomationBench figure in Google’s table. Different benchmark names, task sets and evaluation methods must remain separate even when a screenshot or social post shortens both names to “automation.”
What this suggests for your first pilot
These are evidence-informed hypotheses, not measured recommendations from our own model runs:
| Your task | Candidate to investigate first | What would make the pilot convincing |
|---|---|---|
| Repository issue resolution resembling DeepSWE | Argon, with Astra and Opus as controls | Correct patch, passing relevant tests, no unrelated changes, measured review time |
| More difficult software-engineering tasks resembling FrontierSWE | Astra or Opus, with Argon retained as a challenger | Held-out regression checks and preserved architecture constraints |
| Terminal-heavy tool execution | Opus, compared with Astra and Argon | Correct exit behavior, reproducible commands and useful recovery from failures |
| Very long evidence chains or multimodal analysis | Argon merits a targeted trial | Source/timestamp verification and controlled input size; no assumption that context capacity equals recall |
Choose according to your actual task, access and budget. A published row can prioritize an experiment, but it cannot replace one. Check the Argon access matrix before investing time in a client your account cannot use.
Run a pilot that can reject a model
Download the blank evaluation worksheet (CSV). Use it to record task IDs, repository/input revisions, model settings, usage and verdicts. It contains no model-generated performance claims.
- Select a small representative set of real tasks, including an ordinary case and a failure-prone case. Remove secrets and get the required permission for private inputs. Do not choose only examples already used in vendor demonstrations.
- Freeze the input revision, prompt, tools and allowed runtime. Define acceptance before looking at responses: expected behavior, tests, required citations and prohibited changes.
- Record exact model IDs, reasoning settings, client/SDK versions and retries. If you give one model more time or a different tool, record that difference instead of describing the run as identical.
- Evaluate the artifact, not the explanation. For code, run the relevant tests and inspect the diff. For extraction, compare cells against sources. For video, verify timestamps. A persuasive explanation cannot compensate for a broken output.
- Include rejected attempts in cost and time. Divide total measured cost by the number of accepted results only after defining acceptance consistently. If nothing passes, report no accepted result rather than a misleading cost average.
- Re-run surprising failures with a documented reason. Keep the original result; silently deleting failed runs creates a selected success story, not an evaluation.
Use the API pricing worksheet to separate promotional and December rates. Track human review time separately from token cost. A cheaper run that demands substantially more correction may not be the better workflow.
Why the launch productivity numbers are not your forecast
Google’s Antigravity release account reports a four-week internal observation with 35% more agentic code and 30% fewer rollbacks. Those are Google’s internal observations, not a randomized estimate of the effect for every team.
Likewise, its ADK Python case study describes an approximately 11-hour run and selective acceptance: two patches accepted and four rejected. That is evidence that review remained necessary, not evidence that an overnight autonomous run always produces merge-ready work.
For a defensible model decision, retain the benchmark source and date, match the task, run the pilot, and record why you accepted or rejected the result. The published evidence makes Argon a serious candidate; it does not establish a universal winner.
