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Release notes   Gemini 4 Argon announced. Initial access is limited.Availability snapshot ·
Comparison / published evidence

Gemini 4 Argon vs GPT-6 Astra

A focused comparison of published coding and security results, with practical criteria for your own pilot.

Published snapshot

Four tests. Different outcomes.

Provider-published · not our tests

DeepSWE v1.1

Gemini 4 Argon77.9%
77.9%
GPT-6 Astra74.1%
74.1%

FrontierSWE v2

Gemini 4 Argon55.0%
55%
GPT-6 Astra65.5%
65.5%

Terminal-bench 4.0

Gemini 4 Argon57.4%
57.4%
GPT-6 Astra58.2%
58.2%

CWE-bench v1

Gemini 4 Argon68.0%
68%
GPT-6 Astra68.0%
68%

October 1, 2026 snapshot. Selected percentages from Google's model page. Setup and result sources vary; read the methodology below. No aggregate score is implied.

The published snapshot

Selected percentages from Google DeepMind’s model page, rechecked October 1, 2026. These are reported results, not measurements made by Argon Fieldnotes.

BenchmarkGemini 4 ArgonGPT-6 Astra
DeepSWE v1.177.9%74.1%
FrontierSWE v255.0%65.5%
Terminal-bench 4.057.4%58.2%
CWE-bench v168.0%68.0%

DeepSWE: Argon +3.8 percentage points. FrontierSWE: Astra +10.5. Terminal-bench: Astra +0.8. CWE-bench: displayed tie. Differences are percentage points, not relative percent improvements.

Why this is not a controlled head-to-head test

The evaluation PDF explains how result sources and settings vary. A published score depends on the harness, available tools and benchmark conditions. Small gaps should not be treated as established differences in reliability without uncertainty estimates or a comparable rerun.

The table does not establish latency, account entitlement or document-extraction quality. Published base token rates are compared separately below; total task costs are not independently measured by us.

A useful coding pilot

If repository maintenance is your goal, begin with a reproducible defect and a passing acceptance check. If a terminal workflow is central to your application, include a task requiring the actual tool environment. Keep instructions, starting revisions and budgets equivalent across candidates.

Use the coding prompt and evaluation protocol to record accepted patches and review effort. Test a wider sample before relying on a narrow success. Confirm Argon access and budget repeated attempts using the pricing guide.

Availability, base prices and governance

OpenAI's announcement describes Astra's staged rollout across ChatGPT and API/cloud channels; it does not prove your account is entitled to use it. Argon's initial access remains the limited channel described in its own announcement. OpenAI rollout statement · Argon access.

OpenAI lists Astra pricing starting at $10 input / $50 output per million tokens. Compare those published rates with Argon's announced stages in the price reference, without assuming equal token consumption or total task cost. OpenAI price source.

For a tool-connected pilot, review OpenAI's Astra system card as well as your actual permissions and logging controls. A safety classification is not a general-purpose task score or proof that another model is safer.