Google 发布 Gemini 4 Argon API，输出上限提至 1M token

TL;DR

Size: Parameter count not disclosed (sparse MoE). 1M token window shared by input and output; Runs on: Google’s cloud only, via Gemini API, AI Studio and Gemini platform. Trained on TPUs. No open weights; Performance: Leads Google’s table on knowledge work, long context and video. Mixed on agentic coding; Best: 91.7% on LVBench (long video understanding); Worst: 55.0% on FrontierSWE v2, last of 4 models in Google’s table

What is Gemini 4 Argon?

According to the model card, Argon is a new model, not a fine-tune of an earlier Gemini. It is a large-scale sparse MoE transformer with native multimodal support. It accepts text, images, audio and video, and it outputs text. Google trained it on TPUs using JAX and ML Pathways. The knowledge cutoff is March 2026. Google has not disclosed total or active parameter counts.

How does the 1M token output limit work?

Google raised the output limit to 1M tokens, up from 64K on earlier Gemini models. The model card describes a combined input plus output window of up to 1M tokens. So a long prompt reduces the room left for output. Google documents a new continuation protocol for long decodes in its developer docs and enterprise docs. At introductory pricing, a full 1M token response costs $10 in output tokens.

How does Gemini 4 Argon perform on benchmarks?

Google compares Argon with GPT-6 Astra, Claude Fable 5.1 and Claude Opus 5.5. Argon posts the top score on most rows.

DeepSWE v1.1: 77.9%, ahead of Opus 5.5 (74.2%) and GPT-6 Astra (74.1%); Vals Index: 68.90%, ranked #1 by Vals AI ahead of Claude Sonnet 5.5 (67.04%); AutomationBench: 51.3%, versus 42.5% for Opus 5.5; Harvey’s Legal Agent Benchmark: 19.6%, nearly 3x the next model, but still a low absolute score; GraphWalks, 256K to 1M tokens: 84.2%, versus 71.8% for GPT-6 Astra; CWE-bench v1: 68.0%, tied with GPT-6 Astra

Google also reports 87% on the ERQA embodied reasoning benchmark. Read the evaluation methodology before drawing conclusions. Most rival scores are self-reported or pulled from public leaderboards. Argon’s DeepSWE score is self-computed with a mini-swe-agent harness. The rival DeepSWE numbers come from system cards and a leaderboard.

Where does Gemini 4 Argon fall short?

Argon trails on 5 rows of Google’s own table:

FrontierSWE v2: 55.0%, versus 65.5% for GPT-6 Astra; Terminal-Bench 4.0: 57.4%, versus 66.4% for Opus 5.5; Terminal-Bench Science 0.1: 57.6%, versus 68.1% for GPT-6 Astra; PostTrainBench: 45.3%, versus 49.3% for Opus 5.5; OSWorld-2.0 (offline subset): 69.2%, versus 72.6% for GPT-6 Astra

The model card also lists hallucinations and occasional slowness or timeouts. Under Google’s Frontier Safety Framework, Google says it cannot rule out the biological and cybersecurity critical capability levels. It reports deploying safeguards it assesses as lowering that risk.

Gemini 4 Argon vs GPT-6 Astra vs Claude Opus 5.5 vs Claude Fable 5.1

What did Google use Argon for before launch?

Google shared internal results in its launch post:

Argon in Antigravity raised agentic code submissions by over 35% and cut rollbacks by nearly a third; It delivered a 2.85x end-to-end vLLM inference speedup for Gemma 4 E2B on an A100; In Google’s open-source Agent Development Kit, it cut session prep time by 40%. Cold imports got 21% faster; On Gray Swan’s indirect prompt injection benchmark, attack success fell to 0.7% across 15 adaptive attempts

How much does Gemini 4 Argon cost, and where can you use it?

Access paths, per the developer post:

Key Takeaways

Argon is a sparse MoE model with a 1M token output limit; It ranks #1 on the Vals Index at 68.90%; Intro pricing is $2/$10 per million tokens until November 30; It trails rivals on FrontierSWE v2 and Terminal-Bench 4.0; Most rival scores in Google’s table are self-reported

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