# Gemini 3 Pro Google · Proprietary · rank 31 · 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/gemini-3-pro JSON: https://modelscale.dev/api/model/gemini-3-pro ## Facts | Field | Value | Evidence | | --- | --- | --- | | Canonical id | `gemini-3-pro` | — | | Overall score | 66.7 | Observed 2026-09-22 · source benchlm:models | | Context window | 2M tokens | Observed 2026-09-22 · source benchlm:models | | Release date | 2025-11-18 | Observed 2026-09-22 · source benchlm:models | | Access type | Proprietary | — | | Blended $/1M (75% input / 25% output) | $4.50 | 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 | 61.6 | Observed 2026-09-22 · source benchlm:models | | Knowledge | 82.1 | Observed 2026-09-22 · source benchlm:models | | Reasoning | 36.7 | Observed 2026-09-22 · source benchlm:models | | Multimodal & Grounded | 74.2 | Observed 2026-09-22 · source benchlm:models | | Instruction Following | 84.7 | Observed 2026-09-22 · source benchlm:models | | Math | 55.2 | Observed 2026-09-22 · 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 | 32.65 s | 2026-09-22 | 2026-09-22 | Observed 2026-09-22 · source benchlm:speed | | Throughput | 109 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 | $2.00 | Observed 2026-09-22 · source benchlm:pricing | | Output / 1M tokens | $12.00 | Observed 2026-09-22 · source benchlm:pricing | | Cache read / 1M tokens | Unavailable | Unavailable · source benchlm:pricing | | Cache write / 1M tokens | Unavailable | Unavailable · source openrouter:pricing | | Blended / 1M (75% input / 25% output) | $4.50 | 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.** Google's Gemini Developer API pricing page lists Gemini 3 Pro Preview at $2.00 input / $12.00 output per million tokens for prompts up to 200K tokens, rising to $4.00 / $18.00 above 200K. 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 | $12.67 | | Modelled tokens | 2.82M | ## Benchmark record 26 matched benchmark rows with their published value, unit, and provenance. ### Knowledge | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | AA MMLU-Pro (Artificial Analysis MMLU-Pro) | 89.8 | Professional multi-subject questions | Professional knowledge and reasoning | [Artificial Analysis MMLU-Pro Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/mmlu-pro) | | Artificial Analysis Intelligence Index | 28.0 | Cross-benchmark intelligence index | Display-only external reference | [Artificial Analysis](https://artificialanalysis.ai/) | | AA-GPQA Diamond (Artificial Analysis GPQA Diamond) | 90.8 | 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) | 39.7 | 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) | 15.3 | Knowledge questions | Broad factual knowledge | [AA-Omniscience: Knowledge and Hallucination Benchmark](https://artificialanalysis.ai/evaluations/omniscience) | | AA-Omniscience Accuracy (Artificial Analysis Omniscience Accuracy) | 55.8 | Knowledge questions | Broad knowledge | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) | | AA-Omniscience Hallucination Rate (Artificial Analysis Omniscience Hallucination Rate) | 91.5 | Knowledge questions | Factuality | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) | ### Coding | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | AA LiveCodeBench (Artificial Analysis LiveCodeBench) | 91.7 | Contamination-resistant coding tasks | Competitive programming | [Artificial Analysis LiveCodeBench Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/livecodebench) | | Vibe Code Bench (Vibe Code Bench v1.1) | 14.30 | End-to-end web application builds | End-to-end software delivery | [Vibe Code Bench: Evaluating AI Models on End-to-End Web Application Development](https://www.vals.ai/benchmarks/vibe-code) | ### Mathematics | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | FrontierMath v2 (Tiers 1-3) (FrontierMath v2 Tiers 1-3) | 37.600 | 295 private advanced mathematics problems | From olympiad-plus to early research mathematics | [FrontierMath v2 benchmark hub](https://epoch.ai/benchmarks/frontiermath-tier-4-v2) | | FrontierMath v2 (Tier 4) (FrontierMath v2 Tier 4) | 18.750 | 43 private extreme-difficulty mathematics problems | Research-level mathematics requiring hours or days of expert work | [FrontierMath Tier 4 v2 leaderboard](https://epoch.ai/benchmarks/frontiermath-tier-4-v2?view=graph&tab=leaderboard) | ### Reasoning | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | ARC-AGI-2 (Abstraction and Reasoning Corpus for AGI v2) | 31.1 | Visual pattern completion and abstract reasoning | Expert-level — hardest public reasoning benchmark | [ARC-AGI-2: A Harder General Intelligence Benchmark](https://arcprize.org/arc-agi/2/) | | AA-LCR (Artificial Analysis Long Context Reasoning) | 76.0 | Long-context reasoning tasks | Long-context reasoning | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) | | CritPt (Critical Physics Tasks) | 9.1 | 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) | 70.4 | Verifiable instruction constraints | Instruction precision | [Artificial Analysis IFBench Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/ifbench) | ### Multilingual | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | AA Global-MMLU-Lite (Artificial Analysis Global-MMLU-Lite) | 92.2 | Multilingual knowledge questions | Multilingual professional knowledge | [Artificial Analysis Global-MMLU-Lite Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/global-mmlu-lite) | ### Agentic | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | Gert Labs (Gert Labs Composite Game Benchmark) | 63.23 | Novel game environments | Agentic coding and decision-making | [Gert Labs rankings](https://gertlabs.com/rankings) | | JobBench | 11.4 | 130 tasks across 35 occupations | Professional multi-source workflows | [JobBench: Aligning Agent Work With Human Will](https://arxiv.org/abs/2605.26329) | | τ²-bench results (τ²-Bench Tool-Agent-User Evaluation) | 87.1 | 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 | | --- | --- | --- | --- | --- | | MMMU-Pro (Massive Multi-discipline Multimodal Understanding Pro) | 81 | Multimodal academic reasoning | Frontier multimodal | [MMMU-Pro: A More Robust Multi-discipline Multimodal Understanding Benchmark](https://arxiv.org/abs/2409.02813) | | AA-MMMU-Pro (Artificial Analysis MMMU-Pro) | 80.2 | Multimodal academic reasoning | Frontier multimodal | [Artificial Analysis MMMU-Pro Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/mmmu-pro) | | MathVision | 86.6 | Visually grounded math problems | Advanced multimodal mathematics | [Qwen3.6 launch benchmarks](https://qwen.ai/blog?id=qwen3.6) | | VideoMMMU | 87.6 | Video-grounded expert reasoning | Frontier multimodal video reasoning | [Qwen3.6 launch benchmarks](https://qwen.ai/blog?id=qwen3.6) | | ScreenSpot Pro | 72.7 | 1,581 grounding instructions | Professional GUI grounding | [ScreenSpot-Pro: GUI Grounding for Professional High-Resolution Computer Use](https://arxiv.org/abs/2504.07981) | | V* | 88.0 | Frontier multimodal reasoning tasks | Frontier multimodal | [GLM-5V-Turbo](https://docs.z.ai/guides/vlm/glm-5v-turbo) | | CharXiv (CharXiv Reasoning) | 81.4 | Scientific chart reasoning | Scientific visualization reasoning | [CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs](https://charxiv.github.io/) | ## 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.