# GPT-OSS 20B OpenAI · Open Weight · rank 173 · bench-align-v5 · Self-hosted > Every figure below is reproduced as its upstream source published it: nothing is modelled, estimated, interpolated or converted. `Unavailable` means no source published the value — it is never a zero. Each value carries its evidence state and the date it was observed. Page: https://modelscale.dev/models/gpt-oss-20b JSON: https://modelscale.dev/api/model/gpt-oss-20b ## Facts | Field | Value | Evidence | | --- | --- | --- | | Canonical id | `gpt-oss-20b` | — | | Overall score | 33.23 | Observed 2026-10-23 · source benchlm:models | | Context window | 128K tokens | Observed 2026-10-23 · source benchlm:models | | Release date | 2025-08-05 | Observed 2026-10-23 · source benchlm:models | | Access type | Open Weight | — | | Blended $/1M (75% input / 25% output) | $0.036 | Derived from the input and output rates below | ## Capability evidence Seven axes from the ranking source. An axis the source did not score is unavailable, not zero. | Axis | Score | Evidence | | --- | --- | --- | | Agentic | Unavailable | Unavailable · source benchlm:models | | Coding | 43.1 | Observed 2026-10-23 · source benchlm:models | | Knowledge | 27.4 | Observed 2026-10-23 · source benchlm:models | | Reasoning | 48.8 | Observed 2026-10-23 · source benchlm:models | | Multimodal & Grounded | Unavailable | Unavailable · source benchlm:models | | Instruction Following | 79.2 | Observed 2026-10-23 · source benchlm:models | | Math | Unavailable | Unavailable · source benchlm:models | ## Runtime service evidence Measured values with the date they were observed. Nothing is inferred from a sibling model or a provider claim. Regional or per-endpoint measurements appear only when the API supplies them; none are modelled. | Measurement | Value | Observed | Last good | Evidence | | --- | --- | --- | --- | --- | | Time to first token | 0.65 s | 2026-10-23 | 2026-10-23 | Observed 2026-10-23 · source benchlm:speed | | Throughput | 313 tok/s | 2026-10-23 | 2026-10-23 | Observed 2026-10-23 · source benchlm:speed | ## Endpoint and price matrix Every published price component, including cache reads and writes. | Component | USD | Evidence | | --- | --- | --- | | Input / 1M tokens | $0.018 | Observed 2026-10-23 · source openrouter:pricing | | Output / 1M tokens | $0.09 | Observed 2026-10-23 · source openrouter:pricing | | Cache read / 1M tokens | $0.009 | Observed 2026-10-23 · source openrouter:pricing | | Cache write / 1M tokens | Unavailable | Unavailable · source openrouter:pricing | | Blended / 1M (75% input / 25% output) | $0.036 | Derived — from the input and output rates above; it has no source record of its own | | Cost per successful task (LiveBench) | Unavailable | Fetch failed · source livebench:table | **Self-hosted listing.** Open-weight model. Self-hosted or third-party hosted costs vary by provider and infrastructure. The rates above are a hosted price matched from another provider, not a first-party list price. ## Workload-aware monthly cost example 10 conversations per day × 8 messages × 22 active days, 1200 input and 400 output tokens per message, no cache. Derived here from the published rates above by this site's own calculator — not a figure any source published. | Field | Value | | --- | --- | | Modelled monthly cost | $0.10 | | Modelled tokens | 2.82M | ## Benchmark record 24 matched benchmark rows with their published value, unit, and provenance. ### Knowledge | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | MMLU (Massive Multitask Language Understanding) | 85.3 | 57 subjects | Elementary to professional level | [Measuring Massive Multitask Language Understanding](https://arxiv.org/abs/2009.03300) | | GPQA (Graduate-Level Google-Proof Q&A) | 71.5 | 448 questions | Graduate level | [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://arxiv.org/abs/2311.12022) | | HLE (Humanity's Last Exam) | 10.9 | Expert-level questions | Frontier expert level | [Humanity's Last Exam](https://lastexam.ai/) | | Artificial Analysis Intelligence Index | 9.0 | Cross-benchmark intelligence index | Display-only external reference | [Artificial Analysis](https://artificialanalysis.ai/) | | AA-GPQA Diamond (Artificial Analysis GPQA Diamond) | 68.8 | Graduate-level science questions | Graduate-level science reasoning | [Artificial Analysis GPQA Diamond Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/gpqa-diamond) | | AA-HLE (Artificial Analysis Humanity's Last Exam) | 11.0 | Expert-level questions | Frontier expert reasoning | [Artificial Analysis Humanity's Last Exam Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/hle) | | AA-Omniscience Index (Artificial Analysis Omniscience Index) | -63.0 | Knowledge questions | Broad factual knowledge | [AA-Omniscience: Knowledge and Hallucination Benchmark](https://artificialanalysis.ai/evaluations/omniscience) | | AA-Omniscience Accuracy (Artificial Analysis Omniscience Accuracy) | 16.0 | Knowledge questions | Broad knowledge | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) | | AA-Omniscience Hallucination Rate (Artificial Analysis Omniscience Hallucination Rate) | 94.1 | Knowledge questions | Factuality | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) | ### Coding | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | SWE-bench Verified (Software Engineering Benchmark Verified) | 60.7 | 500 verified issues | Professional software engineering | [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://arxiv.org/abs/2310.06770) | | React Native Evals | 71 | React Native app implementation tasks | Production mobile app engineering | [React Native Evals](https://rn-evals.vercel.app/) | | AA Coding Index (Artificial Analysis Coding Index) | 20.7 | Cross-benchmark coding index | Display-only external reference | [Artificial Analysis model leaderboards](https://artificialanalysis.ai/leaderboards/models) | | AA-SciCode (Artificial Analysis SciCode) | 38.9 | Scientific coding subproblems | Scientific programming | [Artificial Analysis SciCode Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/scicode) | ### Mathematics | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | AIME 2024 (American Invitational Mathematics Examination 2024) | 92.1 | 15 problems | High school olympiad level | [American Invitational Mathematics Examination](https://www.maa.org/math-competitions/aime) | | AIME 2025 (American Invitational Mathematics Examination 2025) | 91.7 | 15 problems | High school olympiad level | [American Invitational Mathematics Examination](https://www.maa.org/math-competitions/aime) | ### Reasoning | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | AA-LCR (Artificial Analysis Long Context Reasoning) | 34.7 | Long-context reasoning tasks | Long-context reasoning | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) | | CritPt (Critical Physics Tasks) | 1.4 | Research-level physics questions | Research-level physics reasoning | [CritPt Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/critpt) | ### Instruction Following | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | AA-IFBench (Artificial Analysis IFBench) | 65.1 | Verifiable instruction constraints | Instruction precision | [Artificial Analysis IFBench Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/ifbench) | ### Agentic | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | GDPval-AA | 514 | Agentic real-world work tasks | Professional agentic workflows | [DeepSeek-V4 Technical Report](https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro/blob/main/DeepSeek_V4.pdf) | | GDPval-AA (GDPval-AA normalized) | 0.0 | Economically valuable tasks | Professional agentic workflows | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) | | AA Agentic Index (Artificial Analysis Agentic Index) | 1.4 | Cross-benchmark agentic index | Display-only external reference | [Artificial Analysis model leaderboards](https://artificialanalysis.ai/leaderboards/models) | | APEX-Agents-AA | 0.7 | 452 professional-services agent tasks | Long-horizon workplace agent tasks | [APEX-Agents-AA Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/apex-agents-aa) | | τ²-bench results (τ²-Bench Tool-Agent-User Evaluation) | 60.2 | Airline, retail, and telecom customer-service task sets | Dual-control customer-service workflows | [τ²-Bench: Evaluating Conversational Agents in a Dual-Control Environment](https://arxiv.org/abs/2506.07982) | ### Multimodal & Grounded | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | Design Arena Website (Design Arena Website Elo) | 854 | Website generation comparisons | Design and website generation | [OpenRouter Grok 4.3 benchmarks](https://openrouter.ai/x-ai/grok-4.3/benchmarks) | ## Lifecycle and limitations log Lifecycle events the source associates with this model. No lifecycle event references this model. That is not evidence the model has no lifecycle plan — only that this source published none. ## What this profile does not claim Values are reproduced exactly as their sources published them, in the units those sources declared; none are converted, interpolated, or averaged across providers. Any field marked unavailable was attempted and not returned. Last attempted fetch for this model's score: 2026-10-23 13:17 UTC.