Gemini 4 Argon vs GPT-6 Astra vs Claude Opus 5.5: Which to Use for Real Work

Gemini 4 Argon vs GPT-6 Astra vs Claude Opus 5.5 has no single winner, and anyone who names one is usually reading a single benchmark. As of October 16, 2026, Opus 5.5 is the safer first pick for terminal and agentic coding and tops an independent index, Argon leads Google’s own table on finance, legal, very long documents and video, and Astra leads the computer-use row at the highest price.

All three vendors claim the top spot, and each is partly right. Google’s model card is vendor-reported, and independent scoring disagrees with it in places. This post maps job types to models, reconciles the two sets of numbers, and gives you a 20-task test to settle the question for your own work.

Everything here comes from vendor pages and Artificial Analysis listings read on October 16, 2026. It is not our own benchmark run, and the numbers will move.

Which is better for your job: Gemini 4 Argon vs GPT-6 Astra vs Claude Opus 5.5?

Pick by job, not by leaderboard. Opus 5.5 is the first model to try for terminal and agentic coding, Argon for finance, legal, very long documents, video and charts, and Astra for computer use and the hardest engineering tasks. The table shows the evidence for each pick and the caveat that should make you test it.

Sources: Google’s Gemini 4 Argon model card for the benchmark rows, and Artificial Analysis for the High-effort rows. For more on the rows themselves, see our Gemini 4 Argon benchmarks breakdown.

Where does Google’s table say Argon leads, and where does independent scoring disagree?

Google’s model card shows Argon first outright on 13 of 19 rows, tied on one and behind on five. Artificial Analysis, an independent evaluator, scores Opus 5.5 at 58 and both Argon and Astra at 53 on its Intelligence Index. The two sets of numbers measure different tasks under different settings, so neither is simply wrong.

The card does not state the thinking settings used and does not flag any row as self-computed. Argon trails on five rows: FrontierSWE v2, Terminal-bench 4.0, PostTrainBench, Terminal-Bench Science and OSWorld-2.0. Opus beats it on the first four of those, and Astra beats it on FrontierSWE v2, Terminal-bench 4.0, Terminal-Bench Science and OSWorld-2.0.

The 58 versus 53 gap also hides a settings mismatch. Artificial Analysis lists Opus 5.5 at Max effort for its 58, and Argon at High, which it calls the highest setting available. Its High-effort comparison page puts the two at 54 and 53, and Argon wins AutomationBench-AA there, 78% to 63%.

Two more findings come from Artificial Analysis’s launch analysis of Argon. Argon’s hallucination rate on AA-Omniscience is 15% against Astra’s 51%, but its accuracy is 50% against 63%, because it answers fewer questions. Argon also averages 62k output tokens per task against Astra’s 27k, so its price advantage comes from cheaper tokens, not fewer of them.

Bloomberg reported in early October 2026 that some Google staff think Gemini 4 scores better on benchmarks than in real coding work, and Google disputed that. Treat it as a reason to test, not as a verdict.

So which numbers should you trust? Use Google’s table to see which jobs Argon was built for. Use Artificial Analysis to compare general capability at matched settings. Trust neither for your own workload.

Gemini 4 Argon vs GPT-6 Astra vs Claude Opus 5.5: what does each cost per task?

Argon lists at $2 per million input tokens and $10 per million output tokens through November 30, 2026, then $4 and $20 from December 1, which matches Opus 5.5. Astra lists at $10 and $50. List price is not cost per task, because Argon spends more tokens than Opus at the same effort level.

The Astra and Opus rows come from OpenAI’s pricing page and Anthropic’s pricing page. Argon’s come from Google’s Gemini API page, covered in our Gemini 4 Argon pricing guide. Astra prompts over 272K input tokens cost twice the input rate and 1.5 times the output rate for the whole request.

Take a hypothetical request with 50,000 input tokens and 10,000 output tokens, thinking included. Argon costs $0.20 before December 1 and $0.40 after. Opus 5.5 costs $0.40 and Astra $1.00. That is the price-sheet view, and Artificial Analysis’s data shows why it misleads.

At High effort, Argon averages 62k output tokens per task against Opus’s 36k, about 1.7 times as many, and 2.3 times Astra’s 27k. At the half price, Argon’s $1.99 per task is already above Opus at High ($1.82). After December 1, Artificial Analysis puts Argon at $3.98, about 2.2 times Opus at High. Opus at Medium ran the index for $1,627 against Argon’s $2,407 at the half price, with a score of 51 against 53.

This is a settings story as much as a price story. Google’s docs say Argon defaults to medium thinking, while the independent numbers above use High. Your bill depends on the setting you actually run.

How do the context windows, output limits and input types compare?

All three models offer roughly a 1M-token context window. Output limits differ: Opus 5.5 and Astra allow 128K tokens per response, while Argon allows 65,536 by default per synchronous request. Only Argon lists video and audio input. Those differences matter more for long reports and media work than for chat.

Long decode is not a free upgrade. It runs in background mode in 64K hops, and each hop resends the prompt plus everything generated so far. Google’s own example turns a 1,000-token prompt with 200,000 output and thinking tokens into 397,216 billed input tokens. Do not use it for chat, extraction or agents that make many short tool calls.

Argon is the model to test for video, charts and scanned PDFs, and our Gemini 4 Argon explainer covers what else it accepts. The newer knowledge cutoff of Astra and Opus matters if your work depends on spring 2026 events.

Which model fits your team’s situation?

If you are unsure, start with Opus 5.5 as your default and add Argon for document-heavy work. That rests on Opus’s lead on the independent index and on terminal tasks, and on Argon’s lead on finance, legal, long-document and video rows. Move to Astra only when computer use or the hardest engineering work justifies its price.

Match your situation to one of these starting points:

• Engineering team building coding agents: test Opus 5.5 first, and run Astra on your hardest tickets. Argon is competitive on DeepSWE v1.1 and Vibe Code Bench in Google’s table but trails on Terminal-bench 4.0 and FrontierSWE v2, so confirm it on your repository.
• Finance, legal or research operations: test Argon first and use Opus as the cross-check. Keep a human reviewer on every output, because these are high-stakes domains and even the best legal score is under 20%.
• Teams automating desktop or browser work: test Astra first. Argon is close on OSWorld-2.0, and its computer use is still in Preview.
• Teams working with video, charts or very long files: test Argon first, then check long-context answers against Opus.
• Cost-constrained, high-volume work: compare Opus at High or Medium with Argon at its default setting, and use Batch pricing where latency allows. Anthropic’s pricing page also lists Claude Sonnet 5.5 at $2 and $10. For the cheaper OpenAI tier, see our GPT-6 Sol vs Claude Opus 5.5 comparison.

For model-by-model detail, read our guides to GPT-6 Astra and Claude Opus 5.5.

How do you test Argon, Astra and Opus 5.5 on your own 20 tasks?

Collect 20 real tasks, run every model on identical prompts and tools, have a reviewer score the outputs blind against a written rubric, and divide total spend by the outputs you would actually accept. Vendor benchmarks tell you where to look first. Only your own tasks show what usable work costs.

Step 1: Build the task set

Pull 20 tasks from the last 90 days of real work, four from each of five buckets that match your jobs, such as coding, document analysis, finance or legal review, long files and tool use. Remove confidential data. Write a “done” checklist for each task before any model runs.

Step 2: Freeze the setup

Use the same prompt, files and tools for every model. Run each at its default effort and at its highest, and record the setting. For Argon, prompting Gemini 4 Argon well can change results, so give each model equal prompt-tuning time.

Step 3: Score blind

Shuffle and unlabel the outputs. Score each from 0 to 2 on the four criteria below. Call an output accepted if correctness is not zero and the total is at least 6 of 8.

Step 4: Log what each run costs

From your usage logs, record input tokens, output tokens including thinking, retries and reviewer minutes for every task.

Step 5: Compute cost per accepted output

Divide total API spend, plus reviewer time at your hourly cost, by the number of accepted outputs. Here is a hypothetical, not a result: Model X costs $30 across 20 tasks and 15 outputs are accepted, or $2.00 each. Model Y costs $18 and only 8 are accepted, or $2.25 each. The cheaper model per token can be the dearer one per usable output.

Twenty tasks will expose large gaps, such as 17 accepted versus 11. They will not separate models that differ by a point or two. If two finish close, rerun that pair on more tasks.

What should you do next?

Pick two models from the matrix: a default for general work and a specialist for your hardest job. Build the 20-task set this week, and score it before you sign any commitment. If Argon is one of the two, finish its run before December 1, then re-cost the results at the higher price.

If you have not yet reached Argon, our guide on how to access Gemini 4 Argon covers the app, API and Antigravity routes. Re-check the Artificial Analysis pages before deciding, because scores and prices on all three models are still moving.