Gemini 4 Argon is a Google closed-API frontier model. CompareLLM Coding Estimated rating is 1630. Active context window extends to 1M tokens. Commercial API token pricing is listed at $2/1M tok input and $10/1M tok output per 1M tokens. Google's Gemini 4 Argon, generally available on the paid Gemini API as of 13 Oct 2026. Announced 30 Sep 2026 with restricted access. Numbers below are dated snapshots from empirical benchmark harnesses.
Independent evaluation answering: "Is Gemini 4 Argon the right model for your workload & budget?"
Listed output price $10.00/1M tokens. Quality is the category rating above, not this price.
Dated snapshot metrics aggregated from official evaluators and API providers with visual relative score bars.
Our category lists (Coding, Reasoning, Chat, Agents). Named suites such as SWE-bench Pro are recipe inputs, not this rating.
LMArena, SWE-bench Pro, LiveBench and similar suites appear with a source and as-of date. They are not CompareLLM's rating.
How much of the recipe is present. Missing inputs stay missing; they are never filled with 0 or 50.
Compact streaming class. Exact tok/s stays in the fact sheet, not in compact cells.
Compact class for time-to-first-token. Exact milliseconds stay in the fact sheet.
Commercial API price per 1 Million prompt/output tokens (~750k words).
| Benchmark Metric & Meaning | Reported Score & Capability Fill | CompareLLM Review |
|---|---|---|
Maximum output (tokens) Maximum Response Length | 944k tokens | Reported citation · Oct 28, 2026Maximum completion tokens for one request on the same endpoint. |
Output token price Cost to Generate (Output tokens) | $10/1M tok | Reported citation · Oct 28, 2026Published output price on the same endpoint. |
Input token price Cost to Prompt (Input tokens) | $2/1M tok | Reported citation · Oct 28, 2026Published input price on the endpoint this sheet is read from. |
Generation speed (tok/s) Writing Speed (tok/s) | <50 tok/s | Reported citation · Oct 28, 2026Median output throughput over OpenRouter's trailing thirty-minute window for this endpoint. |
Context window (tokens) Context Window Capacity (tokens) | 1M tokens | Reported citation · Oct 28, 2026Maximum input context on the same endpoint. |
Time to first token Waiting Time (before it replies) | 2s+ | Reported citation · Oct 28, 2026Median time to first token over the same window for this endpoint. |
Cached input token price Cost to Reuse Cached Input | $0.1/1M tok | Reported citation · Oct 28, 2026Published cache-read price on the same endpoint. |
Peers in the same closed API frontier performance tier — not a jump to an unrelated frontier SKU.
Select any rival to launch a side-by-side empirical benchmark comparison with winner deltas.
Compare Gemini 4 Argon against
Models may use different benchmarks and test settings. This is an indicative composite, not a controlled head-to-head comparison or community Elo. Admin-approved sentiment estimates fill categories without accepted benchmark results. Estimates are labelled and do not increase benchmark coverage. Coverage refers to the configured recipe, not confidence.
Release latest-models-estimated-2026-10-28-r2 · recipe reported-text-2026-09-11-r1 · method reported-with-estimates-v2 · research through 2026-10-28
Overall benchmark coverage includes missing applicable categories. Estimated categories contribute to the rating but add no benchmark coverage.
Editorial estimate, anchored to the Artificial Analysis Intelligence Index v5.0, on which Gemini 4 Argon scores 50 (Gemini 4 Argon (high)), and placed within the 48-66 band this catalog's measured results occupy for this category. v5.0 is a different index from v4.3 and is not this category's recipe, so this is an ordering anchor rather than a measurement. Checked 2026-10-28. Any accepted result that clears the evidence thresholds replaces it.
As of 2026-10-28 · review on 2027-01-28 · Editorial estimate (not a benchmark result)
Sentiment source 1 →Missing benchmark families: repository-engineering, terminal-work, frontiercode.
Editorial estimate, anchored to the Artificial Analysis Intelligence Index v5.0, on which Gemini 4 Argon scores 50 (Gemini 4 Argon (high)), and placed mid-range, because this category has too few measured results to define a band. v5.0 is a different index from v4.3 and is not this category's recipe, so this is an ordering anchor rather than a measurement. Checked 2026-10-28. Any accepted result that clears the evidence thresholds replaces it.
As of 2026-10-28 · review on 2027-01-28 · Editorial estimate (not a benchmark result)
Sentiment source 1 →Missing benchmark families: graduate-science, advanced-mathematics, broad-academic, interactive-abstraction.
Editorial estimate, anchored to the Artificial Analysis Intelligence Index v5.0, on which Gemini 4 Argon scores 50 (Gemini 4 Argon (high)), and placed mid-range, because this category has too few measured results to define a band. v5.0 is a different index from v4.3 and is not this category's recipe, so this is an ordering anchor rather than a measurement. Checked 2026-10-28. Any accepted result that clears the evidence thresholds replaces it.
As of 2026-10-28 · review on 2027-01-28 · Editorial estimate (not a benchmark result)
Sentiment source 1 →Missing benchmark families: constrained-story-writing, instruction-compliance.
Editorial estimate, anchored to the Artificial Analysis Intelligence Index v5.0, on which Gemini 4 Argon scores 50 (Gemini 4 Argon (high)), and placed within the 59-66 band this catalog's measured results occupy for this category. v5.0 is a different index from v4.3 and is not this category's recipe, so this is an ordering anchor rather than a measurement. Checked 2026-10-28. Any accepted result that clears the evidence thresholds replaces it.
As of 2026-10-28 · review on 2027-01-28 · Editorial estimate (not a benchmark result)
Sentiment source 1 →Missing benchmark families: professional-computer-tasks, desktop-use, workflow-automation, web-research.
Editorial estimate, anchored to the Artificial Analysis Intelligence Index v5.0, on which Gemini 4 Argon scores 50 (Gemini 4 Argon (high)), and placed mid-range, because this category has too few measured results to define a band. v5.0 is a different index from v4.3 and is not this category's recipe, so this is an ordering anchor rather than a measurement. Checked 2026-10-28. Any accepted result that clears the evidence thresholds replaces it.
As of 2026-10-28 · review on 2027-01-28 · Editorial estimate (not a benchmark result)
Sentiment source 1 →Missing benchmark families: conversation-quality, support-task-completion, instruction-compliance.
Editorial estimate, anchored to the Artificial Analysis Intelligence Index v5.0, on which Gemini 4 Argon scores 50 (Gemini 4 Argon (high)), and placed mid-range, because this category has too few measured results to define a band. v5.0 is a different index from v4.3 and is not this category's recipe, so this is an ordering anchor rather than a measurement. Checked 2026-10-28. Any accepted result that clears the evidence thresholds replaces it.
As of 2026-10-28 · review on 2027-01-28 · Editorial estimate (not a benchmark result)
Sentiment source 1 →Missing benchmark families: visual-grounding, spatial-reconstruction, visual-symbol-recognition.
What this model is available as, what it takes to run, and where its identity comes from.
The provider's deployment options have not been reviewed for this model.
gemini-4-argongoogle/gemini-4-argonConcise empirical overview formatted for citations and prompt context
Gemini 4 Argon was announced on 30 September with restricted access. On 13 October the Google AI Studio endpoint listed it at $2 input and $10 output per million tokens. Its v5.0 index score, read 28 October, is 50.
Google's September 30 announcement begins a phased release through Fairwind. Broad developer and consumer availability remains a later step.
Plain-English methodology and leaderboard answers
Elo is a crowd vote on which hidden answer people liked more — not a school test. What is preference Elo? · Methodology
Follow this model in your watchlist, set it as your global comparison baseline, or assign it to your custom production stack.
Track updates & rank changes
Compare all models against this
Assign to custom architecture
Compare side-by-side vs all
Plotted against all active catalog models (50th percentile = catalog median).