What Gemini 4 Argon is good at
Google built Argon for long, multi-step professional work. Figures below are reported by Google unless an independent leaderboard is named.
Long-horizon software engineering
Argon scores 77.9% on DeepSWE v1.1, a benchmark of real engineering tasks that take many steps to finish, and Google reports it as the state of the art. Use it for debugging across a large repository, refactors and code migrations.
Finance, legal and business work
Argon is #1 on the Vals Index, an independent leaderboard that weights finance, coding, legal and tax tasks by their share of U.S. GDP. It also ranks first on Zapier's AutomationBench with 51.3%.
Finding and fixing vulnerabilities
Google trained Argon to find, validate and patch software vulnerabilities. It ties for first on CWE-bench v1 with 68%. Point it at code you own and ask it to locate a flaw, explain how it is reached and write the fix.
Charts, documents and long video
Argon reads professional charts, works across a set of documents and picks out details from long videos. It scores 91.7% on LVBench, a long-video understanding benchmark, the highest published result.
One response can run to a million tokens
Earlier Gemini models stopped at 64K output tokens. Argon raises the limit to 1M, so a single response can hold a full report, a long translation or a multi-file change.
Finish long jobs in one pass
Generate a complete document, a whole module or a book-length translation without stitching several responses together and losing context at the seams.
Room to think
With hundreds of thousands of tokens of headroom, Argon can reason through a hard problem and write the answer in a single trajectory instead of stopping half way.
Set max_tokens on purpose
Argon writes long. On the Artificial Analysis index it produced about 62K output tokens per task, against about 27K for GPT-6 Astra. Keep max_tokens low for chat and short answers, and raise it only for jobs that need the room.
What Argon has already done at Google
Google ran Argon on its own engineering work before release. These are the results Google has published.
Rewriting C and C++ in Rust
Argon agents are migrating Google codebases to Rust, from libraries such as re2 up to the 800K-line Fuchsia Zircon kernel. In the libgav1 video decoder they replaced 32K lines of SIMD code, and the result runs 2.7x faster than the earlier Rust port with identical output.
Saving data-center memory
A team of Argon agents read fleet-wide profiling data and applied memory optimizations across Google's data centers, freeing more than 300 TiB.
Quantum algorithm design
Argon helped Google's quantum researchers reduce the qubit and gate cost of key subroutines, beating a published baseline by 40% within minutes.
A vulnerability others missed
Wiz used Argon to uncover a critical flaw in healthcare software used by hospitals worldwide that exposed personal data, a risk earlier frontier models had not found.
Gemini 4 Argon vs Claude Opus 5.5 vs GPT-6 Astra
Three frontier models released within weeks of each other. All three run on AIReiter with the same API key.
| Gemini 4 Argon | Claude Opus 5.5 | GPT-6 Astra | |
|---|---|---|---|
| Developer | Anthropic | OpenAI | |
| Vals Index v2.1 | 68.9% (#1) | 67.0% | 63.1% |
| Max output per response | 1M tokens | 128K tokens | 128K tokens |
| Context window | 1M tokens | 1M tokens | 1.05M tokens |
| Model id on AIReiter | gemini-4-argon | claude-opus-5-5 | gpt-6-astra |
Vals Index scores are from the Vals AI leaderboard as of September 30, 2026. The index weights finance, coding, legal and tax work by economic impact. Argon's lead is clearest when a task needs very long output. On short tasks, send the same prompt to each model and compare the answers.
How to call the Gemini 4 Argon API
AIReiter serves Argon over the OpenAI Chat Completions protocol. An existing OpenAI client needs two changes.
Repoint the client
Set the base URL to https://aireiter.com/api/v1 and use your AIReiter API key. The official OpenAI SDKs work once the base URL is changed.
Send the request
POST https://aireiter.com/api/v1/chat/completions { "model": "gemini-4-argon", "max_tokens": 4096, "messages": [{"role": "user", "content": "Hello"}] }
Size the output
Keep max_tokens modest for chat. Raise it for long reports, migrations and translations, where the 1M output limit is the reason to choose Argon.
Gemini 4 Argon API FAQ
/ 01Which model id do I send?
gemini-4-argon, the same id Google uses in the Gemini API.
/ 02How is Argon different from Gemini 3.x?
Argon is the first Gemini 4 model and the first Gemini model named with a codename instead of a Pro or Flash tier. The biggest practical change is output length: up to 1M tokens per response, up from 64K.
/ 03Do I need a Google account or a Gemini API key?
No. Use your AIReiter key with the AIReiter endpoint. To try the model before writing any code, use the playground on this page.
/ 04Are the benchmark numbers independent?
The Vals Index is an independent leaderboard. DeepSWE, LVBench, CWE-bench and AutomationBench results are reported by Google. Run Argon on your own tasks before you switch a production workload.
/ 05Should I pick Argon or Claude Opus 5.5?
Pick Argon when a job needs very long output, or understanding of charts and long video. For other work, both models run on one AIReiter key, so send the same real task to each and keep the one that does it better.