Gemini 4 Argon vs Gemini 3.1 Pro
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| Comparison | Gemini 4 Argongemini-4-argon | Gemini 3.1 Progemini-3.1-pro-preview |
|---|---|---|
| Type | Chat | Chat |
| Best for | Long-horizon coding, agents and million-token documents | Complex reasoning, multi-step analysis and difficult coding |
| TokenRex price | $2.00 in / $10.00 out per 1M tokens | $2.00 in / $12.00 out per 1M tokens |
| Cached input / 1M tokens | $0.10 per 1M tokens | $0.20 per 1M tokens |
| Cache write / 1M tokens | No extra fee | No extra fee |
| Cached output | Not offered · standard output rate applies | Not offered · standard output rate applies |
| Market price | $2.00 in / $10.00 out per 1M tokens | $2.00 in / $12.00 out per 1M tokens |
| Context window (provider) | 1,048,576 tokens | Not applicable |
| Input token limit (provider) | Within the context window | 1,048,576 tokens |
| Maximum output | 65,536 tokens | 65,536 tokens |
| Knowledge cutoff | March 2026 | — |
| Vendor input modalities | Text, Image, Audio, Video | Text, Image, Audio, Video |
| Vendor output modalities | Text | Text |
| TokenRex endpoints | POST /v1/chat/completions | POST /v1/chat/completions |
| Benchmarks | ||
| Benchmark evidence | 19 source-linked results | 5 source-linked results |
| Vals Index | 68.9% Self-reported by Google in the Gemini 4 Argon model card, October 2026. Google DeepMind · Gemini 4 Argon model card | — |
| AutomationBench | 51.3% Self-reported by Google in the Gemini 4 Argon model card (Score), October 2026. Google DeepMind · Gemini 4 Argon model card | — |
| Vals Finance Agent v2 | 65.4% Self-reported by Google in the Gemini 4 Argon model card, October 2026. Google DeepMind · Gemini 4 Argon model card | — |
| Harvey's Legal Agent Benchmark | 19.6% Self-reported by Google in the Gemini 4 Argon model card, October 2026. Google DeepMind · Gemini 4 Argon model card | — |
| DeepSWE v1.1 | 77.9% Self-reported by Google in the Gemini 4 Argon model card, October 2026. Google DeepMind · Gemini 4 Argon model card | — |
| FrontierSWE v2 | 55% Self-reported by Google in the Gemini 4 Argon model card, October 2026. Google DeepMind · Gemini 4 Argon model card | — |
| Vibe Code Bench | 91.9% Self-reported by Google in the Gemini 4 Argon model card, October 2026. Google DeepMind · Gemini 4 Argon model card | — |
| Terminal-bench 4.0 | 57.4% Self-reported by Google in the Gemini 4 Argon model card, October 2026. Google DeepMind · Gemini 4 Argon model card | — |
| PostTrainBench | 45.3% Self-reported by Google in the Gemini 4 Argon model card, October 2026. Google DeepMind · Gemini 4 Argon model card | — |
| Terminal-Bench Science 0.1 | 57.6% Self-reported by Google in the Gemini 4 Argon model card, October 2026. Google DeepMind · Gemini 4 Argon model card | — |
| LABBench 2 | 88.8% Self-reported by Google in the Gemini 4 Argon model card, October 2026. Google DeepMind · Gemini 4 Argon model card | — |
| RiemannBench | 76% Self-reported by Google in the Gemini 4 Argon model card, October 2026. Google DeepMind · Gemini 4 Argon model card | — |
| GraphWalks BFS (up to 128K) | 99.7% Self-reported by Google in the Gemini 4 Argon model card (F1), October 2026. Google DeepMind · Gemini 4 Argon model card | — |
| GraphWalks BFS (256K to 1M) | 84.2% Self-reported by Google in the Gemini 4 Argon model card (F1), October 2026. Google DeepMind · Gemini 4 Argon model card | — |
| Agent's Last Exam | 39.5% Self-reported by Google in the Gemini 4 Argon model card (Pass rate), October 2026. Google DeepMind · Gemini 4 Argon model card | — |
| OSWorld-2.0 (offline subset) | 69.2% Self-reported by Google in the Gemini 4 Argon model card (Partial score), October 2026. Google DeepMind · Gemini 4 Argon model card | — |
| Chartography | 71.6% Self-reported by Google in the Gemini 4 Argon model card, October 2026. Google DeepMind · Gemini 4 Argon model card | — |
| LVBench | 91.7% Self-reported by Google in the Gemini 4 Argon model card, October 2026. Google DeepMind · Gemini 4 Argon model card | — |
| CWE-bench v1 | 68% Self-reported by Google in the Gemini 4 Argon model card, October 2026. Google DeepMind · Gemini 4 Argon model card | — |
| ARC-AGI-2 | — | 77.1% ARC Prize Verified, semi-private set, self-computed by Google per official gemini_3-1_pro_model_evaluation.pdf methodology (pass@1, single attempt, default gemini-3.1-pro-preview sampling). Published source text: On ARC-AGI-2 ... 3.1 Pro achieved a verified score of 77.1%. This is more than double the reasoning performance of 3 Pro. Google Blog – Gemini 3.1 Pro: A smarter model for your most complex tasks |
| GPQA Diamond | — | 94.3% Self-computed by Google per official Gemini 3.1 Pro model-evaluation methodology PDF (pass@1, single-attempt, gemini-3.1-pro-preview default settings, Feb 2026). Published source text: 94.3% on GPQA Diamond NxCode – Gemini 3.1 Pro Complete Guide 2026 (citing Google launch benchmarks) |
| SWE-bench Verified | — | 80.6% Self-computed per official methodology PDF: single-attempt scaffolding, averaged over 10x runs, gemini-3.1-pro-preview, Feb 2026. Published source text: 80.6% on SWE-Bench Verified NxCode – Gemini 3.1 Pro Complete Guide 2026 (citing Google launch benchmarks) |
| LiveCodeBench Pro Elo | — | 2887elo Public leaderboard Elo score as reported in Google's official Gemini 3.1 Pro model-evaluation PDF (Feb 2026). Published source text: LiveCodeBench Pro | 2887 Elo | Coding evaluation S5 Labs – Gemini 3.1 Pro Leads 12 of 18 Benchmarks |
| BrowseComp | — | 85.9% Deep Research configuration (search, python, browsing access) per official Gemini 3.1 Pro evaluation methodology, Feb 2026. Published source text: BrowseComp | 85.9% | Web browsing comprehension S5 Labs – Gemini 3.1 Pro Leads 12 of 18 Benchmarks |
No overall score, winner or cost-versus-score frontier is calculated without comparable, attributable benchmark evidence. Different test configurations appear separately. Vendor capabilities are distinct from TokenRex API support.
— means no published value or no matching capability. Provider token limits are not tested gateway guarantees; TokenRex media controls and response formats are listed separately.
Gemini 4 Argon
Sources and verification
Provider specifications describe the upstream model, not a guarantee of every feature through TokenRex. Prices come from the enabled catalog; benchmark results belong to the exact tested model.
- Gemini API · Gemini 4 Argon model specifications
Vendor-reported
Last checked - Google DeepMind · Gemini 4 Argon model card
Vendor-reported
Last checked - Google Blog · Introducing Gemini 4 Argon
Vendor-reported
Last checked
Research notes (2)
- Published provider limits; maximum-capacity requests have not been independently exercised through TokenRex. Standard requests allow up to 65,536 output tokens. Google's long-decode mode (output up to 1,048,576 tokens minus input) is enabled with background=true — the TokenRex chat endpoint accepts this parameter (verified live on 2026-10-14), but a full 1M-token output has not been exercised, so the default 65,536-token output limit is shown.
- All benchmark scores are self-reported by Google in the official model card; TokenRex has not re-run them.
Gemini 3.1 Pro
Sources and verification
Provider specifications describe the upstream model, not a guarantee of every feature through TokenRex. Prices come from the enabled catalog; benchmark results belong to the exact tested model.
- Provider model specifications
Vendor-reported
Last checked - Google Blog – Gemini 3.1 Pro: A smarter model for your most complex tasks
Vendor-reported
Last checked - NxCode – Gemini 3.1 Pro Complete Guide 2026 (citing Google launch benchmarks)
Third-party report
Last checked - S5 Labs – Gemini 3.1 Pro Leads 12 of 18 Benchmarks
Third-party report
Last checked
Research notes (1)
- Published provider limits; maximum-capacity requests have not been independently exercised through TokenRex. Thinking-level variants use the same provider model; reasoning and answer tokens share the output budget.
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