OpenAI: gpt-oss-120b (batch)
openai/gpt-oss-120b:batch
Model weightsgpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized to run on a single H100 GPU with native MXFP4 quantization. The model supports configurable reasoning depth, full chain-of-thought access, and native tool use, including function calling, browsing, and structured output generation.
Providers
Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick: Balanced (price + speed), Nitro (fastest), or Floor (cheapest).
| $0.0296 | $0.136 | -- | -- |
Input $0.0296 / Output $0.136-- tok/s-- uptime
Pricing
The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.
Weighted average
gpt-oss-120b (batch) — Price history
| $0.00 | $0.00 | 0.0% | 100.0% |
- Effective in $0.00 / Effective out $0.00Token share100.0%
Performance
Throughput is how fast the model writes (tokens per second — higher is better). Latency is total round-trip time (lower is better). TTFT is time-to-first-token — how long before you see anything appear (lower is better).
Uptime
Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.
Not enough uptime data to display yet.
When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and provider routing options.
Benchmarks
Scores on standardized evaluations and this model’s own outputs on shared benchmark prompts. Higher percentages are better — and each rank shows where this model lands among the models evaluated on that source.
| Source | Benchmark | Score |
|---|---|---|
| Artificial Analysis | gpt-oss-120b (High) Intelligence Index | 3.8 |
| Artificial Analysis | gpt-oss-120b (High) Coding Index | 30.4 |
| Artificial Analysis | gpt-oss-120b (High) Agentic Index | 3.7 |
| Artificial Analysis | gpt-oss-120b (High) GPQA Diamond | 78.2% |
| Artificial Analysis | gpt-oss-120b (High) HLE | 19.6% |
| Artificial Analysis | gpt-oss-120b (High) IFBench | 69.0% |
| Artificial Analysis | gpt-oss-120b (High) τ²-Bench Telecom | 65.8% |
| Artificial Analysis | gpt-oss-120b (High) AA-LCR | 52.0% |
| Artificial Analysis | gpt-oss-120b (High) τ-Bench Banking | 12.8% |
| Artificial Analysis | gpt-oss-120b (High) GDPval-AA | 5.8% |
| Artificial Analysis | gpt-oss-120b (High) CritPt | 1.1% |
| Artificial Analysis | gpt-oss-120b (High) LiveCodeBench | 87.8% |
| Artificial Analysis | gpt-oss-120b (High) SciCode | 34.0% |
| Artificial Analysis | gpt-oss-120b (High) Terminal-Bench Hard | 23.5% |
| Artificial Analysis | gpt-oss-120b (High) Terminal-Bench 4.0 | 0.0% |
| Artificial Analysis | gpt-oss-120b (High) Terminal-Bench 2.1 | 26.2% |
| Artificial Analysis | gpt-oss-120b (High) MMLU-Pro | 80.8% |
| Artificial Analysis | gpt-oss-120b (High) AA-Omniscience Accuracy | 21.7% |
| Artificial Analysis | gpt-oss-120b (High) AA-Omniscience Non-Hallucination Rate | 13.1% |
| Artificial Analysis | gpt-oss-120b (High) AIME 2025 | 93.4% |
| Artificial Analysis | gpt-oss-120b (Low) Coding Index | 21.2 |
| Artificial Analysis | gpt-oss-120b (Low) GPQA Diamond | 67.2% |
| Artificial Analysis | gpt-oss-120b (Low) HLE | 5.9% |
| Artificial Analysis | gpt-oss-120b (Low) IFBench | 58.3% |
| Artificial Analysis | gpt-oss-120b (Low) τ²-Bench Telecom | 45.0% |
| Artificial Analysis | gpt-oss-120b (Low) AA-LCR | 46.0% |
| Artificial Analysis | gpt-oss-120b (Low) τ-Bench Banking | 2.9% |
| Artificial Analysis | gpt-oss-120b (Low) GDPval-AA | 0.0% |
| Artificial Analysis | gpt-oss-120b (Low) CritPt | 0.0% |
| Artificial Analysis | gpt-oss-120b (Low) LiveCodeBench | 70.7% |
| Artificial Analysis | gpt-oss-120b (Low) Terminal-Bench Hard | 5.3% |
| Artificial Analysis | gpt-oss-120b (Low) Terminal-Bench 2.1 | 13.9% |
| Artificial Analysis | gpt-oss-120b (Low) MMLU-Pro | 77.5% |
| Artificial Analysis | gpt-oss-120b (Low) AIME 2025 | 66.7% |
| Design Arena | GPT OSS 120B Models Arena 3D Elo | 881 |
| Design Arena | GPT OSS 120B Models Arena Code Categories Elo | 952 |
| Design Arena | GPT OSS 120B Models Arena Data Visualization Elo | 984 |
| Design Arena | GPT OSS 120B Models Arena Game Development Elo | 981 |
| Design Arena | GPT OSS 120B Models Arena UI Component Elo | 917 |
| Design Arena | GPT OSS 120B Models Arena Website Elo | 960 |
| Vals AI | GPT OSS 120B AIME | 92.6% |
| Vals AI | GPT OSS 120B CaseLaw v2 | 48.8% |
| Vals AI | GPT OSS 120B CorpFin v2 | 58.2% |
| Vals AI | GPT OSS 120B Finance Agent v1.1 | 21.5% |
| Vals AI | GPT OSS 120B GPQA Diamond | 78.5% |
| Vals AI | GPT OSS 120B LiveCodeBench | 83.2% |
| Vals AI | GPT OSS 120B LegalBench | 75.9% |
| Vals AI | GPT OSS 120B MATH 500 | 94.8% |
| Vals AI | GPT OSS 120B MedQA | 91.4% |
| Vals AI | GPT OSS 120B MGSM | 92.0% |
| Vals AI | GPT OSS 120B MMLU Pro | 79.2% |
| Vals AI | GPT OSS 120B SWE-bench | 33.6% |
| Vals AI | GPT OSS 120B TaxEval v2 | 71.6% |
| Vals AI | GPT OSS 120B Terminal-Bench 2.0 | 19.1% |
Apps
Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.
Not enough data for apps using this model yet.
Activity
Token volume and request traffic to this model over time.
API
Drop-in code to call this model. OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. The only thing that changes between models is the model slug below.
Not currently available through EU in-region routing. In-region routing docs
Documentation
Guides, cookbooks, blog posts, and tools for building with OpenAI: gpt-oss-120b (batch) on OpenRouter.
Frequently asked questions
gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized to run on a single H100 GPU with native MXFP4 quantization.
The gpt-oss-120b (batch) documentation on OpenRouter includes the LLM Quantization: What fp8 and fp4 Do to Quality and Price blog post. The quickstart covers API keys and your first request, and the API reference lists every parameter.
gpt-oss-120b (batch) costs $0.0296/M input tokens and $0.136/M output tokens.
gpt-oss-120b (batch) has a 131,072 token context window. It supports up to 131,072 completion tokens.
Yes. gpt-oss-120b (batch) accepts tools and tool_choice for function calling. It also supports structured outputs via a JSON schema in response_format.
gpt-oss-120b (batch) is served by 1 provider on OpenRouter: DeepInfra.
GPT-6.1 Terra Pro, GPT-6.1 Terra, GPT-6 Luna Decisions and 58 more are other text models from OpenAI.
gpt-oss-120b (batch) was released on August 5, 2025. Its knowledge cutoff is June 30, 2024.