Gemini 3.1 Flash LitevsGPT OSS 120B
GPT OSS 120B is cheaper than Gemini 3.1 Flash Lite at $0.18/M vs $0.25/M input tokens.
| Spec | Gemini 3.1 Flash Lite | GPT OSS 120B |
|---|---|---|
| Input price | $0.25/M tokens | $0.18/M tokens |
| Output price | $1.5/M tokens | $0.8/M tokens |
| Context window | 1.0M tokens | 131K tokens |
| Weights | Closed | Open |
| Free API (OpenRouter) | No | No |
| Released | May 2026 | Aug 2025 |
Our Verdict
Gemini 3.1 Flash Lite
GPT OSS 120B
Not enough votes to call it. On the specs, nothing separates them.
Too close to call
Reviewing agent-written code?See a Brief PR report
API pricing
Cost per 1M tokens
Gemini 3.1 Flash Lite
Input
$0.25
Output
$1.50
GPT OSS 120B
Input
$0.18
1.4× cheaper
Output
$0.80
1.9× cheaper
GPT OSS 120B is cheaper on both: 1.4× input, 1.9× output.
Where to run it
21 hosts, cheapest first
Gemini 3.1 Flash Lite2 hosts
GPT OSS 120B19 hosts
AkashMLbf16$0.03 in$0.17 out131k100% upCoreWeavefp4$0.03 in$0.17 out131k99.9% upDekaLLMbf16$0.03 in$0.18 out131k98.9% upDeepInfrabf16$0.04 in$0.17 out131k99.9% upNovitafp4$0.05 in$0.25 out131k95% upDigitalOcean$0.06 in$0.39 out128k100% up
13 more hostsFewer hosts
Mancerfp8$0.06 in$0.50 out131k98.9% upGoogle 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 out131k95.8% upGroq$0.15 in$0.60 out131k100% upNebiusfp4$0.15 in$0.60 out131k99.7% upPhala$0.15 in$0.60 out131k95.3% upTogether$0.15 in$0.60 out131k95.5% upCerebrasfp16$0.35 in$0.75 out131k100% upSiliconFlowfp8degraded$0.15 in$0.60 out131k97% upMaradegraded$0.15 in$0.75 out131k70.4% up
Per million tokens. Prices and uptime via OpenRouter, checked 12 Sep 2026.
Writing DNA
Style Comparison
Similarity
43%
GPT OSS 120B uses 15.4x more emoji
Gemini 3.1 Flash Lite
GPT OSS 120B
59%Vocabulary52%
17wSentence Length19w
0.29Hedging0.28
5.5Bold7.4
3.3Lists1.8
0.00Emoji0.15
0.93Headings0.73
0.11Transitions0.17
Based on 27 + 21 text responses
Research
What we learned reading every model
FAQ
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