openai/gpt-oss-20b:batch
Model weightsgpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for lower-latency inference and deployability on consumer or single-GPU hardware. The model is trained in OpenAI’s Harmony response format and supports reasoning level configuration, fine-tuning, and agentic capabilities including function calling, tool use, and structured outputs.
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).
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.
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 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.
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-20b (Low) Intelligence Index | 10.0 |
| Artificial Analysis | gpt-oss-20b (Low) Math Index | 62.3 |
| Artificial Analysis | gpt-oss-20b (Low) GPQA Diamond | 61.1% |
| Artificial Analysis | gpt-oss-20b (Low) HLE | 5.3% |
| Artificial Analysis | gpt-oss-20b (Low) IFBench | 57.8% |
| Artificial Analysis | gpt-oss-20b (Low) τ²-Bench Telecom | 50.3% |
| Artificial Analysis | gpt-oss-20b (Low) AA-LCR | 31.0% |
| Artificial Analysis | gpt-oss-20b (Low) CritPt | 0.0% |
| Artificial Analysis | gpt-oss-20b (Low) LiveCodeBench | 65.2% |
| Artificial Analysis | gpt-oss-20b (Low) Terminal-Bench Hard | 4.5% |
| Artificial Analysis | gpt-oss-20b (Low) MMLU-Pro | 71.8% |
| Artificial Analysis | gpt-oss-20b (Low) AA-Omniscience Accuracy | 15.1% |
| Artificial Analysis | gpt-oss-20b (Low) AA-Omniscience Non-Hallucination Rate | 13.3% |
| Artificial Analysis | gpt-oss-20b (Low) AIME 2025 | 62.3% |
| Artificial Analysis | gpt-oss-20b (High) Intelligence Index | 9.0 |
| Artificial Analysis | gpt-oss-20b (High) Coding Index | 20.7 |
| Artificial Analysis | gpt-oss-20b (High) Math Index | 89.3 |
| Artificial Analysis | gpt-oss-20b (High) Agentic Index | 1.2 |
| Artificial Analysis | gpt-oss-20b (High) GPQA Diamond | 68.8% |
| Artificial Analysis | gpt-oss-20b (High) HLE | 11.0% |
| Artificial Analysis | gpt-oss-20b (High) IFBench | 65.1% |
| Artificial Analysis | gpt-oss-20b (High) τ²-Bench Telecom | 60.2% |
| Artificial Analysis | gpt-oss-20b (High) AA-LCR | 34.7% |
| Artificial Analysis | gpt-oss-20b (High) τ-Bench Banking | 7.0% |
| Artificial Analysis | gpt-oss-20b (High) GDPval-AA | 0.0% |
| Artificial Analysis | gpt-oss-20b (High) CritPt | 1.4% |
| Artificial Analysis | gpt-oss-20b (High) LiveCodeBench | 77.7% |
| Artificial Analysis | gpt-oss-20b (High) SciCode | 38.9% |
| Artificial Analysis | gpt-oss-20b (High) Terminal-Bench Hard | 10.6% |
| Artificial Analysis | gpt-oss-20b (High) Terminal-Bench 4.0 | 0.0% |
| Artificial Analysis | gpt-oss-20b (High) Terminal-Bench 2.1 | 13.9% |
| Artificial Analysis | gpt-oss-20b (High) MMLU-Pro | 74.8% |
| Artificial Analysis | gpt-oss-20b (High) AA-Omniscience Accuracy | 16.0% |
| Artificial Analysis | gpt-oss-20b (High) AA-Omniscience Non-Hallucination Rate | 5.9% |
| Artificial Analysis | gpt-oss-20b (High) AIME 2025 | 89.3% |
| Design Arena | GPT OSS 20B Models Arena Data Visualization Elo | 933 |
| Design Arena | GPT OSS 20B Models Arena Website Elo | 852 |
| Vals AI | GPT OSS 20B AIME | 86.0% |
| Vals AI | GPT OSS 20B CaseLaw v2 | 43.8% |
| Vals AI | GPT OSS 20B CorpFin v2 | 53.1% |
| Vals AI | GPT OSS 20B GPQA Diamond | 68.9% |
| Vals AI | GPT OSS 20B LiveCodeBench | 80.4% |
| Vals AI | GPT OSS 20B LegalBench | 70.8% |
| Vals AI | GPT OSS 20B MATH 500 | 94.2% |
| Vals AI | GPT OSS 20B MedQA | 82.9% |
| Vals AI | GPT OSS 20B MGSM | 89.0% |
| Vals AI | GPT OSS 20B MMLU Pro | 71.6% |
| Vals AI | GPT OSS 20B TaxEval v2 | 63.7% |
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.
Token volume and request traffic to this model over time.
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
gpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for lower-latency inference and deployability on consumer or single-GPU hardware.
gpt-oss-20b (batch) costs $0.024/M input tokens and $0.112/M output tokens.
gpt-oss-20b (batch) has a 131,072 token context window. It supports up to 131,072 completion tokens.
Yes. gpt-oss-20b (batch) accepts tools and tool_choice for function calling. It also supports structured outputs via a JSON schema in response_format.
GPT-6 Luna Decisions, GPT-6.1 Sol Pro, GPT-6.1 Sol and 62 more are other text models from OpenAI.
gpt-oss-20b (batch) was released on August 5, 2025. Its knowledge cutoff is June 30, 2024.