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Gemini 4 Argon benchmarks, pricing and specs

Gemini 4 Argon is a Google model released on September 30, 2026 that takes text, image and video input and produces text. It costs $2 per 1M input tokens and $10 per 1M output tokens. It scores 1525 Elo on Arena Text, #1 of 49 current models (Arena, measured October 8, 2026).

Per 1M input tokens
$2
Per 1M output tokens
$10
Arena Text
1525 Elo · #1/49

Data verified October 13, 2026 · Dataset 2026-10-13

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Key facts

Provider
Google
Model family
Gemini 4
Release date
September 30, 2026
Status
Current
Access
API only
API model id
Not reported
Context window
Not reported
Max output
1,000,000 tokens
Input
text, image, video
Output
text
Task families
text, multimodal
Declared capabilities
reasoning, coding, agentic coding, image understanding, video understanding, document understanding

Gemini 4 Argon pricing

It is the #13 most expensive of 47 current text models priced per 1M input tokens.

UnitPrice (USD)TierAppliesSource
1M input tokens$2standardCurrent
1M output tokens$10standardCurrent
1M input tokens$0.10cachedCurrent
1M input tokens$4after introductoryCurrent
1M output tokens$20after introductoryCurrent

Independent benchmark results

Scores come from independent evaluators that ran the model themselves, pinned to a dated snapshot. Rank is among current models in the same snapshot. Google's own launch figures are listed under strengths with the publisher named, because they are not measured the same way.

BenchmarkScoreRankMeasuredSource
Arena Text1525 Elo#1 of 49October 8, 2026Arena · 2026-10-12

Strengths, cautions and best uses

Strengths

  • Google reports 77.9% on DeepSWE v1.1 for long-horizon software engineering and first place on AutomationBench at 51.3%
  • Raises the output limit to 1M tokens, up from 64K on earlier Gemini models
  • Ties for the top Arena Text rating in this catalogue at its announcement

Cautions

  • Access is limited: Google is rolling it out first to cyber defenders in its Fairwind Program, and no Gemini API model id or pricing page entry exists yet
  • The $2 input and $10 output rate is introductory with no end date; Google says $4 and $20 per 1M tokens applies afterwards
  • Google has not published the context window

Best for

  • Long-horizon coding and knowledge work once it reaches general API access

Alternatives to Gemini 4 Argon

The closest current textmodels by price. The cheapest model that scores as well or better is listed first; failing that, the cheapest one within 3% of the benchmark's scale.

Compare Gemini 4 Argon with

Articles about Gemini 4 Argon

Gemini 4 Argon FAQ

How much does Gemini 4 Argon cost?

Gemini 4 Argon costs $2 per 1M input tokens and $10 per 1M output tokens at the standard tier. It is the #13 most expensive of 47 current text models priced per 1M input tokens. Source: Google, verified October 13, 2026.

How does Gemini 4 Argon score on independent benchmarks?

Gemini 4 Argon scores 1525 Elo on Arena Text, #1 of 49 current models (Arena, measured October 8, 2026). These are independent evaluations, not Google's own launch figures.

Is Gemini 4 Argon open source or API only?

Gemini 4 Argon is available through Google's API only; its weights are not published.

What are the best alternatives to Gemini 4 Argon?

The closest current alternatives by price and measured performance are Claude Sonnet 5 (same price per 1M input tokens, $2), Claude Sonnet 5.5 (same price per 1M input tokens, $2), GPT-4.1 (same price per 1M input tokens, $2) and GPT-5.6 Terra (same price per 1M input tokens, $2).

Sources

Cite this page

Van Data Team. "Gemini 4 Argon Benchmarks, Pricing and Specs." AI Model Benchmark Dataset 2026-10-13. https://vandatateam.com/ai-model-benchmarks/gemini-4-argon

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Production evaluation

Deciding whether Gemini 4 Argon fits your workload?

Public benchmarks narrow the shortlist. We test the finalists on your own tasks, data and cost limits, then build the routing and review controls to run the winner in production.

  • Evaluation cases and acceptance thresholds tied to real work
  • Model routing, fallbacks and cost controls