[whatmodel.sh](../../README.md) / Gemini 4 Argon # Gemini 4 Argon Google · released 2026-09 · proprietary **score 80.6** ±1.3 · #9 of 732 · from 5 benchmark results across 2 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Gemini 4 Argon among every model on that task or test, worst to best. 
task            vs the others        rank  counts  tests
agent tasks     ▁▁▃▆█▄▅▅▄▄●▁   #7 of 313*     48%      4
coding          ▁▁▁▃▅▇█▇▅▄●▁   #9 of 469*     35%      2
chat & writing  ▁▁▁▃▆▄▆█▆▇▇●   #2 of 402*     17%      1
agent tasks · #7 of 313 · 48% of its score Beats [GPT-6.1 Terra](gpt-6-1-terra.md), [Muse Wonder Full](muse-wonder-full.md) here, though they rank higher overall. 
test                                    result  vs the others in its run  difficulty            counts  why
Arena Agent (October 2026 aggrega…  0.1073 IPS  ▁▁▁▃▃▂█▁▂●▁▁ beats 53/59  avg -0.379 IPS           43%  sharp
DeepSWE v1.1 (resolved) (maker)          77.9%  ▂····▂▁▁▆██● beats 31/31  hard · avg 25%          4.4%  small run
Terminal-Bench 4.0 (maker)               57.4%  ▁·▁··▁▆▆▅█●▃ beats 27/33  very hard · avg 0.5%       —  not counted yet: no other model in the …
Agents' Last Exam (pass@1, offici…       39.5%  █····▄·····● beats 3/3                               —  not counted yet: no other model in the …
coding · #9 of 469 · 35% of its score Beats [GPT-6.1 Terra](gpt-6-1-terra.md) here, though they rank higher overall. Trails [Claude Opus 5](claude-opus-5.md) here, though they rank lower overall. 
test                             result  vs the others in its run    difficulty      counts  why
Arena WebDev (Code Arena)        1679.2  ▁▁▃▂▅█▃▆▅●▁▂ beats 120/129  avg 981            30%  sharp
DeepSWE v1.1 (resolved) (maker)   77.9%  ▂····▂▁▁▆██● beats 31/31    hard · avg 25%    4.4%  small run
chat & writing · #2 of 402 · 17% of its score Beats [GPT-6 Astra](gpt-6-astra.md), [Claude Opus 5.5](claude-opus-5-5.md), [GPT-6.1 Sol](gpt-6-1-sol.md) here, though they rank higher overall. 
test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1520.3  ▁▁▁▂▃▃▃▆▆▇█● beats 384/385  avg 1260       17%  blunt
other tests (1) 
test             result  vs the others in its run  difficulty        counts  why
LVBench (maker)   91.7%  ▇·█▅▅▄▄··▃▁● beats 28/28  medium · avg 59%    4.2%  typical
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
LMArena leaderboard dataset                     3   91% ▇▇▇▇▇▇▇▇▇▇▇
Google DeepMind Gemini model page and m…        2    9% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 80.6
Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
Not counted (2) 
source                  test                            score  why
Google DeepMind Gemin…  Terminal-Bench 4.0              57.4%  too few models have it yet
Google DeepMind Gemin…  Agents' Last Exam (pass@1, of…  39.5%  too few models have it yet
## How to run it ### Through an API api (Google) · $4.00/M blended · $2 in / $10 out per 1M tokens · [ai.google.dev](https://ai.google.dev/gemini-api/docs/models/gemini-4-argon) [price source](https://ai.google.dev/gemini-api/docs/pricing): USD per 1M tokens, Gemini API Standard paid tier for gemini-4-argon, introductory price through 2026-11-30 (output includes thinking tokens); it rises to $4.00 input and $20.00 output on 2026-12-01. Cached input $0.10 (implicit caching only) and batch prices excluded; no prompt-length tiers; not on the Free tier. Google official pricing page, observed 2026-10-14. · [context source](https://ai.google.dev/gemini-api/docs/models/gemini-4-argon) [interactive version](https://whatmodel.sh/model/gemini-4-argon) · [JSON](https://whatmodel.sh/api?format=json&all=1)