# o3-mini OpenAI · Proprietary · rank 141 · bench-align-v5 > 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/o3-mini JSON: https://modelscale.dev/api/model/o3-mini ## Facts | Field | Value | Evidence | | --- | --- | --- | | Canonical id | `o3-mini` | — | | Overall score | 47.03 | Observed 2026-09-22 · source benchlm:models | | Context window | 200K tokens | Observed 2026-09-22 · source benchlm:models | | Release date | 2025-01-31 | Observed 2026-09-22 · source benchlm:models | | Access type | Proprietary | — | | Blended $/1M (75% input / 25% output) | $1.93 | 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 | 23 | Observed 2026-09-22 · source benchlm:models | | Knowledge | 66.3 | Observed 2026-09-22 · source benchlm:models | | Reasoning | Unavailable | Unavailable · source benchlm:models | | Multimodal & Grounded | Unavailable | Unavailable · source benchlm:models | | Instruction Following | Unavailable | Unavailable · 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 | 7.12 s | 2026-09-22 | 2026-09-22 | Observed 2026-09-22 · source benchlm:speed | | Throughput | 160 tok/s | 2026-09-22 | 2026-09-22 | Observed 2026-09-22 · source benchlm:speed | ## Endpoint and price matrix Every published price component, including cache reads and writes. | Component | USD | Evidence | | --- | --- | --- | | Input / 1M tokens | $1.10 | Observed 2026-09-22 · source benchlm:pricing | | Output / 1M tokens | $4.40 | Observed 2026-09-22 · source benchlm:pricing | | Cache read / 1M tokens | $0.55 | Observed 2026-09-22 · source openrouter:pricing | | Cache write / 1M tokens | Unavailable | Unavailable · source openrouter:pricing | | Blended / 1M (75% input / 25% output) | $1.93 | Derived — from the input and output rates above; it has no source record of its own | | Cost per successful task (LiveBench) | Unavailable | Unavailable · source livebench:table | **Self-hosted listing.** OpenAI's o3-mini model page lists $1.10 input / $4.40 output per million tokens. 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 | $5.42 | | Modelled tokens | 2.82M | ## Benchmark record 9 matched benchmark rows with their published value, unit, and provenance. ### Knowledge | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | MMLU (Massive Multitask Language Understanding) | 86.9 | 57 subjects | Elementary to professional level | [Measuring Massive Multitask Language Understanding](https://arxiv.org/abs/2009.03300) | | GPQA (Graduate-Level Google-Proof Q&A) | 77.2 | 448 questions | Graduate level | [GPQA: A Graduate-Level Google-Proof Q&A Benchmark](https://arxiv.org/abs/2311.12022) | | Artificial Analysis Intelligence Index | 12.5 | Cross-benchmark intelligence index | Display-only external reference | [Artificial Analysis](https://artificialanalysis.ai/) | | AA-GPQA Diamond (Artificial Analysis GPQA Diamond) | 74.8 | 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) | 7.9 | Expert-level questions | Frontier expert reasoning | [Artificial Analysis Humanity's Last Exam Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/hle) | ### Coding | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | SWE-bench Verified (Software Engineering Benchmark Verified) | 49.3 | 500 verified issues | Professional software engineering | [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://arxiv.org/abs/2310.06770) | ### Mathematics | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | AIME 2024 (American Invitational Mathematics Examination 2024) | 87.3 | 15 problems | High school olympiad level | [American Invitational Mathematics Examination](https://www.maa.org/math-competitions/aime) | ### Instruction Following | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | IFEval (Instruction-Following Eval) | 93.9 | 541 prompts across 25 instruction types | Instruction precision | [Instruction-Following Evaluation for Large Language Models](https://arxiv.org/abs/2311.07911) | ### Agentic | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | τ²-bench results (τ²-Bench Tool-Agent-User Evaluation) | 28.7 | 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) | ## 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: 2026-09-22 10:17 UTC.