The short answer: Astra for hard agent work, Argon for price and long context
Choose GPT-6 Astra when the task is hard multi-step coding or computer use and the extra cost is justified; Google DeepMind's table lists it ahead on those tests. Choose Gemini 4 Argon for high-volume work or long-context reasoning, where it costs $2 input and $10 output per million tokens until November 30, against Astra's $10 and $50.
Scope and evidence
Task: choosing a frontier model for coding, agents and long documents. Argon facts come from the Gemini API model and pricing pages; Astra facts from OpenAI's model page; benchmarks from the comparison table on Google DeepMind's Gemini page, where, according to Google's methodology document, results for non-Gemini models are the providers' self-reported numbers. Pages read between October 30 and November 1, 2026.
AppFor.ai has not run Argon or Astra for this comparison.
Compare the same criteria
Each line pairs Google's figure for Argon with OpenAI's documentation or the same Google table for Astra.
- API price per million tokens. Argon: $2 input, $10 output until November 30, 2026, then $4 and $20. Astra: $10 input, $50 output; prompts over 272K input tokens cost more. What this means: Argon is 5 times cheaper now and 2.5 times after the introductory period.
- Context and output. Argon: 1,048,576-token context. Astra: 1.05M context, 128K max output. What this means: equal for long inputs.
- Artificial Analysis Intelligence Index, per Google's table. Argon: 50. Astra: 57. What this means: Astra leads overall.
- FrontierSWE 2 and Terminal-Bench 4.0. Argon: 55.0% and 57.1%. Astra: 65.5% and 59.1%. What this means: a clear Astra lead on FrontierSWE, a small one on Terminal-Bench.
- OSWorld computer use. Argon: 74.7%. Astra: 80.9%. What this means: Astra ahead for agents that operate apps.
- GraphWalks long-context reasoning. Argon: 84.2%. Astra: 71.8%. What this means: Argon's main advantage.
- Other Argon leads in Google's table. AutomationBench 51.3% vs 41.4%, Vals Index v2.2 69.2% vs 65.0%, and the multimodal tests Chartography (71.6% vs 71.0%) and Blueprint-Bench 2 (54.4% vs 49.7%). What this means: Argon is ahead on knowledge-work automation and chart and diagram understanding.
Which fits your real work?
A team running thousands of document summaries a day. Argon fits on price, after a quality check on a sample.
A developer delegating hard multi-file changes to an agent. Astra fits on the published coding scores.
A ChatGPT Plus user. Astra is included in Work and Codex on Plus with limited usage; more Astra use needs credits. Argon needs Google AI Pro or Ultra.
Cost of getting the work done
API: 2 million input and 200,000 output tokens cost $6 on Argon until November 30 and $12 after, and $30 on Astra.
Apps: Astra is included in ChatGPT Plus ($20 a month, limited usage in Work and Codex, credits beyond that), Pro, Business and Enterprise; Argon in Google AI Pro and Ultra, with prices varying by country.
Try both on the same small task
Suggested check: run the same coding task and the same long-document task on both APIs.
- Every cited line exists and supports the claim.
- The test fails before and passes after the fix.
- Record tokens and cost per run.
- Repeat each run three times.
Decision checklist
Use the lines that match your situation. The Gemini 4 Argon and GPT-6 Astra profiles give the full specifications.
- If cost per task dominates, start with Argon.
- If coding or computer-use success dominates, start with Astra.
- If you work over very long linked records, test Argon first.
- If you are on ChatGPT Plus or higher, Astra is already in your plan.
- Re-check Argon's price after November 30, 2026.
Go to the source
Sources & further reading
Based on the official documentation below. Features and access can change; check the provider before making a decision.
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