Gemini 3.6 FlashvsGPT OSS 120B
GPT OSS 120B is cheaper than Gemini 3.6 Flash at $0.18/M vs $1.5/M input tokens.
| Spec | Gemini 3.6 Flash | GPT OSS 120B |
|---|---|---|
| Input price | $1.5/M tokens | $0.18/M tokens |
| Output price | $7.5/M tokens | $0.8/M tokens |
| Context window | 1.0M tokens | 131K tokens |
| Weights | Closed | Open |
| Free API (OpenRouter) | No | No |
| Released | Jul 2026 | Aug 2025 |
Our Verdict
Gemini 3.6 Flash
GPT OSS 120B
Not enough votes to call it. On the specs, nothing separates them.
GPT OSS 120B costs 9.4x less per token.
Too close to call
Reviewing agent-written code?See a Brief PR report
API pricing
Cost per 1M tokens
Gemini 3.6 Flash
Input
$1.50
Output
$7.50
GPT OSS 120B
Input
$0.18
8.3× cheaper
Output
$0.80
9.4× cheaper
GPT OSS 120B is cheaper on both: 8.3× input, 9.4× output.
Where to run it
21 hosts, cheapest first
Gemini 3.6 Flash2 hosts
GPT OSS 120B19 hosts
AkashMLbf16$0.03 in$0.17 out131k100% upCoreWeavefp4$0.03 in$0.17 out131k100% upDekaLLMbf16$0.03 in$0.18 out131k99% upDeepInfrabf16$0.04 in$0.17 out131k99.9% upDigitalOcean$0.06 in$0.39 out128k100% upMancerfp8$0.06 in$0.50 out131k98.9% up
13 more hostsFewer hosts
Google Vertex AI$0.09 in$0.36 out131k99.9% upBasetenfp4$0.10 in$0.50 out128k100% upParasailfp4$0.10 in$0.75 out131k100% upSambaNova$0.14 in$0.95 out131k97.9% upAmazon Bedrock$0.15 in$0.60 out131k100% upGroq$0.15 in$0.60 out131k100% upNebiusfp4$0.15 in$0.60 out131k99.7% upSiliconFlowfp8$0.15 in$0.60 out131k97% upTogether$0.15 in$0.60 out131k97.2% upCerebrasfp16$0.35 in$0.75 out131k100% upNovitafp4degraded$0.05 in$0.25 out131k95.2% upPhaladegraded$0.15 in$0.60 out131k95.8% upMaradegraded$0.15 in$0.75 out131k73.1% up
Per million tokens. Prices and uptime via OpenRouter, checked 12 Sep 2026.
Writing DNA
Style Comparison
Similarity
59%
GPT OSS 120B uses 3.7x more emoji
Gemini 3.6 Flash
GPT OSS 120B
60%Vocabulary52%
19wSentence Length19w
0.13Hedging0.28
5.4Bold7.4
2.7Lists1.8
0.04Emoji0.15
0.99Headings0.73
0.07Transitions0.17
Based on 27 + 21 text responses
Research
What we learned reading every model
FAQ
Common questions
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