Gemini 4 Argon
gemini-4-argongoogle/gemini-4-argon- 🏢 Google's newest Gemini text model, released October 13, 2026.
- 📏 Unified 1,048,576-token budget shared across input, thinking, and output.
- 🧠 Tunable thinking levels: low, medium (default), and high.
- 🔧 Built for long-horizon coding, migrations, and self-built verification harnesses.
- 🔒 Cyber-permissive variant defaults to the high thinking level.
- 👁️ Accepts text, images, audio and video; function calling and grounding.
- 🎯 Described in its model card as a new frontier model.
- 🌐 Available through the Gemini API, AI Studio, and Google Cloud.
Google is an American multinational technology corporation and one of the world's most valuable brands. A subsidiary of parent company Alphabet Inc., Google operates across search, cloud computing, consumer electronics, and artificial intelligence. Its DeepMind and Google…
Explore 28 more models by Google →Gemini 4 Argon is Google's newest Gemini text model, available to developers and enterprises from October 13, 2026. Its model card describes it as a new frontier model rather than a fine-tune of any prior Gemini, and Google positions it for "deep work" in complex, real-world tasks spanning coding, finance, legal, cybersecurity and science.
Relative to its predecessor Gemini 3.1 Pro Preview, the most concrete architectural change is context handling: Argon uses a single 1,048,576-token limit shared across input and output, where output includes reasoning traces, function calls and the final response, instead of the separate input and output limits used by Gemini 3.1 Pro. The reasoning-budget parameter is also replaced by discrete thinking levels — low, medium, or high — with medium the default and high the default on the cyber-permissive variant.
Within the wider family, Argon sits above the workhorse Flash tier represented by Gemini 3.8 Flash, which Google describes as tuned for long-horizon coding and autonomous agents. Practically, Argon is aimed at hours-long runs: legacy refactors, cross-language migrations, and vulnerability remediation where the model builds and iterates against its own tests. Multimodal inputs — text, images, audio and video — plus function calling and search grounding let it operate as the reasoning core of agentic workflows.
This About section is AI-generated from public sources via VeniceStats + Venice inference, with no human editing. It may contain inaccuracies.
Daily usage · Gemini 4 Argon · Organic sellers
Top sellers · Gemini 4 Argon · Organic
- zro94.4%
- ZLKPro-Api2.5%
- Best0.9%
- Hana Gateway ✅0.7%
- Super Seeder0.5%
- OpenCheap0.4%
- Others0.7%
| Seller | Health | Rep↓ | Sales | Tokens | Routing | Input $/M | Cached $/M | Output $/M | Save | 1st token | tok/s |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Super SeederSAME 0xd19f…41f3 | healthy | 89.22 | $4.28 | 3.80M | #3 | $3.1251 | $0.1563 | $15.6253 | — | — | — |
| 💬 BanterSAME 0x4122…e194 | healthy | 82.88 | $0.55 | 309.14k | #2 | $1.39 | $0.0695 | $6.95 | −31% | — | — |
| ▲ Apex AntSAME 0x73b4…e736 | — | 81.71 | $1.27 | 734.79k | #1 | $1.25 | $0.06 | $6.25 | −38% | — | — |
| zro 0x8398…2c06 | healthy | 59.20 | $739.35 | 250.05M | gated | $1.80 | $0.09 | $9.00 | −10% | — | — |
| attacompute.aiSAME 0xd5e7…7be0 | healthy | 51.22 | $0.26 | 72.80k | gated | $1.90 | $0.095 | $9.50 | −5% | — | — |
| ZLKPro-ApiSAME 0x0b0b…f446 | healthy | 20.91 | $19.21 | 160.44M | gated | $0.11 | $0.06 | $0.60 | −95% | — | — |
| Unified Rack · DeepSeek 240 tok/sSAME 0x42d1…822f | healthy | 13.21 | $0.25 | 68.03k | gated | $2.1979 | $0.09 | $13.1875 | — | — | — |
| OpenCheapSAME 0x0ee1…6c41 | — | 12.43 | $2.87 | 14.50M | gated | $0.1078 | $0.0588 | $0.588 | −95% | — | — |
| Fire Ant 🔥🐜SAME 0xbe05…bc5d | — | 0.00 | $2.06 | 816.25k | gated | $1.89 | $0.08 | $9.49 | −6% | — | — |
| LLMartSAME 0x948a…a000 | — | 0.00 | $0.53 | 199.49k | gated | $1.1682 | $0.0743 | $5.8212 | −42% | — | — |
| Edith AIDIFF 0xb269…b1a6 | healthy | 0.00 | —excluded | — | gated | $1.1682 | $0.0594 | $5.8212 | −42% | — | — |
| Hana Gateway ✅SAME 0x4ae1…117b | healthy | 0.00 | $5.33 | 24.28M | gated | $0.20 | $0.04 | $1.00 | −90% | — | — |
| BestSAME 0x472f…69fd | healthy | 0.00 | $7.16 | 67.22M | gated | $0.1129 | $0.0384 | $0.6306 | −94% | 0.4 s | 125 |
"Best price" and the seller table are live Antseed catalog data (advertised $/1M tokens — or $ per generated image for unit-billed image models — not settled amounts). Reputation = buyer trust score (0-100, the Antseed SDK's own formula). "Routing" = the SDK's default buyer routing (what the VPR desktop app ships with): a trust ≥ 60 gate on the effective reputation, then cheapest-first among routable sellers; live failover state (per-peer cooldowns) is buyer-side runtime and not included. Model knowledge (TLDR, provider, About) via the VeniceStats enrichment layer. Advertised catalog, not the model used in any specific purchase. "Usage on Antseed" counts only settlements whose buyers share the per-model split on-chain (metadata v2/v3, opt-in), so every usage figure is a lower bound.