# Gemini 3.6 Flash Google · Proprietary · rank 23 · 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-3-6-flash JSON: https://modelscale.dev/api/model/gemini-3-6-flash ## Facts | Field | Value | Evidence | | --- | --- | --- | | Canonical id | `gemini-3-6-flash` | — | | Overall score | 68.62 | Observed 2026-09-22 · source benchlm:models | | Context window | 1M tokens | Observed 2026-09-22 · source benchlm:models | | Release date | 2026-07-21 | Observed 2026-09-22 · source benchlm:models | | Access type | Proprietary | — | | Blended $/1M (75% input / 25% output) | $3.00 | 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 | 80.2 | Observed 2026-09-22 · source benchlm:models | | Coding | 58.7 | Observed 2026-09-22 · source benchlm:models | | Knowledge | 78.2 | Observed 2026-09-22 · source benchlm:models | | Reasoning | 76 | Observed 2026-09-22 · source benchlm:models | | Multimodal & Grounded | 82.6 | Observed 2026-09-22 · 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 | 25.82 s | 2026-09-22 | 2026-09-22 | Observed 2026-09-22 · source benchlm:speed | | Throughput | 219 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 | $1.50 | Observed 2026-09-22 · source benchlm:pricing | | Output / 1M tokens | $7.50 | Observed 2026-09-22 · source benchlm:pricing | | Cache read / 1M tokens | $0.15 | Observed 2026-09-22 · source benchlm:pricing | | Cache write / 1M tokens | Unavailable | Unavailable · source openrouter:pricing | | Blended / 1M (75% input / 25% output) | $3.00 | 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-3.6-flash at $1.50 input / $0.15 cached input / $7.50 output per million tokens on the standard paid tier. 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 | $8.45 | | Modelled tokens | 2.82M | ## Benchmark record 22 matched benchmark rows with their published value, unit, and provenance. ### Knowledge | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | Artificial Analysis Intelligence Index | 34.0 | Cross-benchmark intelligence index | Display-only external reference | [Artificial Analysis](https://artificialanalysis.ai/) | | AA-GPQA Diamond (Artificial Analysis GPQA Diamond) | 92.8 | 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) | 40.8 | 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) | 22.1 | Knowledge questions | Broad factual knowledge | [AA-Omniscience: Knowledge and Hallucination Benchmark](https://artificialanalysis.ai/evaluations/omniscience) | | AA-Omniscience Accuracy (Artificial Analysis Omniscience Accuracy) | 50.0 | Knowledge questions | Broad knowledge | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) | | AA-Omniscience Hallucination Rate (Artificial Analysis Omniscience Hallucination Rate) | 55.6 | Knowledge questions | Factuality | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) | | GPQA Diamond (Vals) (GPQA Diamond, Vals AI run) | 93.4 | Graduate-level science questions | Expert reasoning | [Vals AI GPQA Diamond, Vals AI run leaderboard](https://www.vals.ai/benchmarks/gpqa) | | MMLU-Pro (Vals) (MMLU-Pro, Vals AI run) | 89.3 | Academic multiple-choice questions | Broad academic knowledge | [Vals AI MMLU-Pro, Vals AI run leaderboard](https://www.vals.ai/benchmarks/mmlu_pro) | ### Coding | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | AA Coding Index (Artificial Analysis Coding Index) | 69.2 | Cross-benchmark coding index | Display-only external reference | [Artificial Analysis model leaderboards](https://artificialanalysis.ai/leaderboards/models) | | AA-SciCode (Artificial Analysis SciCode) | 53.4 | Scientific coding subproblems | Scientific programming | [Artificial Analysis SciCode Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/scicode) | | LiveCodeBench (Vals) (LiveCodeBench, Vals AI run) | 88.1 | Competitive programming problems (easy, medium, hard) | Frontier coding | [Vals AI LiveCodeBench, Vals AI run leaderboard](https://www.vals.ai/benchmarks/lcb) | | SWE-bench (Vals) (SWE-bench, Vals AI run) | 79.6 | Real repository issues by human time bucket | Frontier coding agents | [Vals AI SWE-bench, Vals AI run leaderboard](https://www.vals.ai/benchmarks/swebench) | | DeepSWE | 49.0 | 113 software engineering tasks across 91 repositories and 5 languages | Long-horizon software engineering | [DeepSWE benchmark blog](https://deepswe.datacurve.ai/blog) | ### Reasoning | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | AA-LCR (Artificial Analysis Long Context Reasoning) | 80.0 | Long-context reasoning tasks | Long-context reasoning | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) | | CritPt (Critical Physics Tasks) | 10.6 | Research-level physics questions | Research-level physics reasoning | [CritPt Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/critpt) | ### Agentic | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | GDPval-AA | 1423 | 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) | 38.2 | Economically valuable tasks | Professional agentic workflows | [Artificial Analysis model benchmarks](https://artificialanalysis.ai/models/grok-4-3) | | AA Agentic Index (Artificial Analysis Agentic Index) | 30.1 | Cross-benchmark agentic index | Display-only external reference | [Artificial Analysis model leaderboards](https://artificialanalysis.ai/leaderboards/models) | | OSWorld-Verified | 83 | 369 real-world computer tasks (361 when eight Google Drive tasks are excluded) | Multi-step desktop and cross-application workflows | [OSWorld](https://os-world.github.io/) | | Terminal-Bench 2.1 (Vals) (Terminal-Bench 2.1, Vals AI run) | 73.8 | Difficult terminal tasks | Frontier agentic | [Vals AI Terminal-Bench 2.1, Vals AI run leaderboard](https://www.vals.ai/benchmarks/terminal-bench-2-1) | ### Multimodal & Grounded | Benchmark | Value | Tasks | Difficulty | Provenance | | --- | --- | --- | --- | --- | | AA-MMMU-Pro (Artificial Analysis MMMU-Pro) | 83.2 | Multimodal academic reasoning | Frontier multimodal | [Artificial Analysis MMMU-Pro Benchmark Leaderboard](https://artificialanalysis.ai/evaluations/mmmu-pro) | | Design Arena Website (Design Arena Website Elo) | 1310 | 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: 2026-09-22 10:17 UTC.