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Gemini 3.8 Flash (high)

★ featured
Google · released 2026-09-02 · 3 offers

Output 306 tokens/sFirst token 9.5 sContext 1M tokens

Top 2 cheapest providers (Adjusted $/task)

The same list price can give a different adjusted $/task (caching, token efficiency) — click a price for its inputs.

Within the active global provider, residency and confidentiality filters.

#ProviderAdjusted $/task
1Google
2Google Vertex AI

Composite

6 of 7 inputs · 3 from the model family6 radar axes: DesignArena's two boards share one

81.8

includes −5.8 for its Benchmaxxing signal (from 87.6; why, switch off in Options)

Benchmaxxing signal +5.8, light Benchmaxxing tag · light report →

AA CodingCoding Agent v1.4AA IntelligenceAA AgenticEpoch ECISoftware ECIDesignArenaAA Coding: percentile 93Coding Agent v1.4: percentile 62AA Intelligence: percentile 95AA Agentic: percentile 79Epoch ECI: percentile 77DesignArena: percentile 61

AA Coding 76.3Coding Agent v1.4 60.2AA Intelligence 41.2AA Agentic 41.1Epoch ECI 156.5Software ECI DesignArena 1256/1250

Radar: percentile among all models measured on each input; a gap means not measured.

Benchmark sheet

44 of 140 registered benchmark versions · bars show the percentile among all models measured on each benchmark.

Composite attachments (used in the score, not counted as exact benchmarks):
  • Epoch ECI · attached
  • DesignArena Web Apps (agentic) · attached
  • DesignArena Full-Stack · attached
Compare this model →

Agentic

  • AA-Briefcase671,202 Elo

    AA-Briefcase published 2026-09-10 · Published board — Tests multi-week professional knowledge-work projects with linked tasks and large source collections.

  • GDPval-AA v2841,464 Elo

    GDPval-AA v2 v2 · Published board — Tests professional knowledge-work deliverables across occupations using AA's Stirrup harness.

  • Terminal-Bench v2.1 (AA)9387.6%

    Terminal-Bench v2.1 (AA) v2.1 · Published board — Tests terminal-based work on the 89-task verified refresh using Terminus 2.

  • Terminal-Bench v4.0 (AA)8119.7%

    Terminal-Bench v4.0 (AA) v4.0 · Published board — Tests terminal-based work on the 66-task release using mini-SWE-agent v2.4.6.

  • ApprenticeBench API (NeoCognition)7734.0%

    ApprenticeBench API (NeoCognition) published 2026-09-14 · Claude Code — An agent completes the same 100 sequential accounts-payable tasks in the Odoo ERP with diminishing mentoring, using dedicated application API calls instead of the screen interface.

  • Vals Index v2 (Vals AI)8062.3%

    Vals Index v2 (Vals AI) v2 · Published board — GDP-weighted average of agentic model performance across finance, coding and legal tasks.

  • Finance Agent v2 (Vals Index v2)10061.4%

    Finance Agent v2 (Vals Index v2) v2 · Published board — Multi-step financial reasoning tasks.

  • Terminal-Bench 2.1 (Vals Index v2)8781.3%

    Terminal-Bench 2.1 (Vals Index v2) v2 · Published board — Command-line interface problem solving, as run by Vals AI.

  • HLAB — Harvey's Legal Agent Benchmark (Vals Index v2)7510.0%

    HLAB — Harvey's Legal Agent Benchmark (Vals Index v2) v2 · Published board — Long-horizon legal work product creation. Vals AI's run of Harvey's Legal Agent Benchmark, reported as accuracy — not comparable with Artificial Analysis' Harvey LAB-AA row.

  • AA Agentic Index7941.1

    AA Agentic Index published 2026-09-18 · Published board — Artificial Analysis publishes one Agentic Index per model, measured on its primary configuration and relayed by OpenRouter's Benchmarks API; the value is attached at family scope on the deterministic representative. Artificial Analysis publishes this board without a version number; we keep the date each result was retained.

Coding

  • Artificial Analysis Coding Agent Index v1.4no percentile59.1%

    Artificial Analysis Coding Agent Index v1.4 v1.4 · Antigravity SDK v0.1.12 — The retained AA Coding Agent Index measures coding-agent systems using the earlier three-component implementation.

  • Artificial Analysis Coding Agent Index v1.410061.2%

    Artificial Analysis Coding Agent Index v1.4 v1.4 · Opencode — The retained AA Coding Agent Index measures coding-agent systems using the earlier three-component implementation.

  • Artificial Analysis Coding Agent Index v1.5no percentile41.9%

    Artificial Analysis Coding Agent Index v1.5 v1.5 · Antigravity SDK v0.1.12 — Measures coding-agent systems on DeepSWE v1.1, Terminal-Bench 4.0 and SWE-Atlas-QnA.

  • SciCode (AA subproblems) v1.0.18956.6%

    SciCode (AA subproblems) v1.0.1 · Published board — Tests scientific Python programming with scientist-annotated background information.

  • CursorBench 4.0 (Cursor) v4.0no percentile39.6%

    CursorBench 4.0 (Cursor) v4.0 · Published board — Agent evaluation on ambiguous, multi-file tasks drawn from real Cursor sessions.

  • Vibe Code Bench (Vals Index v2)6878.7%

    Vibe Code Bench (Vals Index v2) v2 · Published board — End-to-end app-building tasks.

  • Code Migration (Vals Index v2 subset)4431.6%

    Code Migration (Vals Index v2 subset) v2 · Published board — Porting projects to another language, including COBOL modernization.

  • FrontierCode 1.1 Main (Cognition) v1.1no percentile38.0%

    FrontierCode 1.1 Main (Cognition) v1.1 · chisel — Whether a maintainer would merge the agent's pull request, on tasks crafted by open-source maintainers and graded with tests, rubrics and verifiers.

  • DeepSWE (Datacurve, via Epoch AI)9873.8%

    DeepSWE (Datacurve, via Epoch AI) published 2026-09-15 · mini-swe-agent — Pass rate of coding agents on original, long-horizon software engineering tasks, run by Datacurve with the mini-swe-agent harness.

  • WeirdML v307.4%

    WeirdML v3 v3 · Published board — Agentic benchmark with 11 hand-made machine-learning tasks: a model must explore unfamiliar data, build and run analysis pipelines and produce results despite limited data, unspecified goals or very limited feedback.

  • KernelBench-CUDA: DeepSeek NSA (RTX PRO 6000) vrtx-pro-6000409.58

    KernelBench-CUDA: DeepSeek NSA (RTX PRO 6000) vrtx-pro-6000 · Published board — Implement DeepSeek’s Native Sparse Attention block (block top-n routing + sparse attention) as a CUDA kernel over a frozen shape sweep.

  • KernelBench-CUDA: MegaQwen Decode (RTX PRO 6000) vrtx-pro-6000404.26

    KernelBench-CUDA: MegaQwen Decode (RTX PRO 6000) vrtx-pro-6000 · Published board — Improve the known MegaQwen CUDA megakernel geometry for decode-only token throughput at context lengths 2k–128k; prefill is untimed.

  • KernelBench-CUDA: Grid MinGRU SPS (RTX PRO 6000) vrtx-pro-60003336.4

    KernelBench-CUDA: Grid MinGRU SPS (RTX PRO 6000) vrtx-pro-6000 · Published board — Non-LLM RL simulation: maximise simulation steps per second for a grid world with a MinGRU agent (roofline anchored at 150M peak SPS); fusion optional.

  • AA Coding Index9376.3

    AA Coding Index published 2026-09-19 · Published board — Artificial Analysis publishes this board without a version number; we keep the date each result was retained.

  • DesignArena Web Apps (agentic)751,256 Elo

    DesignArena Web Apps (agentic) published 2026-09-19 · Published board — Published Elo. Exact source attachment is retained; fewer than 200 battles excludes a row from radar normalization and anomaly peers.

  • DesignArena Full-Stack701,250 Elo

    DesignArena Full-Stack published 2026-09-19 · Published board — Published Elo. Exact source attachment is retained; fewer than 200 battles excludes a row from radar normalization and anomaly peers.

Efficiency

Knowledge

Long-context

  • GDP.pdf (AA)8021.0%

    GDP.pdf (AA) published 2026-09-10 · Published board — Tests professional reasoning over long PDFs with AA document preparation and grading.

  • AA-LCR v1.19381.3%

    AA-LCR v1.1 v1.1 · Published board — Tests reasoning across multiple long documents with corrected answer keys and grading.

  • MLCR-AA7621.7%

    MLCR-AA published 2026-09-10 · Published board — Tests medical-record synthesis and reasoning across long, fragmented case documents.

Math

  • FrontierMath Tiers 1–3 v2 (Epoch AI)5868.4%

    FrontierMath Tiers 1–3 v2 (Epoch AI) v2 · Published board — Unpublished, expert-written mathematics problems from undergraduate to research level with automatically checkable answers, run by Epoch AI on its private v2 set.

  • FrontierMath Tier 4 v2 (Epoch AI)1122.0%

    FrontierMath Tier 4 v2 (Epoch AI) v2 · Published board — The hardest, research-level tier of Epoch AI's unpublished FrontierMath problems, run by Epoch AI on its private v2 set.

Reasoning

  • Chess Puzzles (Epoch AI)9861.0%

    Chess Puzzles (Epoch AI) published 2026-09-18 · Published board — Best-move selection on 100 novel chess positions generated programmatically by Epoch AI, each with a single Stockfish-verified best move; probes spatial reasoning and planning.

  • Mystery Game Puzzles (Epoch AI)8147.0%

    Mystery Game Puzzles (Epoch AI) published 2026-09-18 · Published board — Best-move selection on 100 mid-game positions of a well-known game whose identity Epoch deliberately keeps undisclosed, generated programmatically like Chess Puzzles.

  • AA Intelligence Index9541.2

    AA Intelligence Index published 2026-09-19 · Published board — Artificial Analysis publishes this board without a version number; we keep the date each result was retained.

Science

Tool-use

  • AutomationBench-AA v1.0.69259.9%

    AutomationBench-AA v1.0.6 · Published board — Tests multi-app SaaS workflows through REST tools on a held-out AutomationBench split.

  • τ³-Banking (AA) v1.0.19344.9%

    τ³-Banking (AA) v1.0.1 · Published board — Tests banking support agents that retrieve policies and change account state through tools.

  • Excel Modeling Benchmark (Vals Index v2)8872.2%

    Excel Modeling Benchmark (Vals Index v2) v2 · Published board — Building and editing financial models in spreadsheets.

Vision

Missing coverage · 97 benchmark versions

No result does not mean a zero, or that the model was never tested. Collection failures and disputed versions retain their distinct status.

Variants / reasoning settings

Artificial Analysis snapshot 2026-09-19 · Data: Artificial Analysis · Data: Epoch AI (CC BY)

VariantAA CodingAA Intelligence
Gemini 3.8 Flash (high)76.341.2
Gemini 3.8 Flash (medium)74.140.0
Gemini 3.8 Flash (low)73.533.8

SUBSCRIPTION PLAN

GitHub Copilot

Current usage-based billing · model token cost is converted to AI Credits at 1 credit = $0.01.

Input $0.750 / 1M
Cached input $0.075 / 1M
Output $3.75 / 1M
Status GA

Official promotional token rates through December 31, 2026; no post-promotion rate inferred.

Token offers by platform · 2 offers (Adjusted $/task)

Click any underlined price to see how it is estimated and where each input comes from. How we calculate adjusted cost.

2 offers within the active global filters; “—” means the catalog is active but no public token price is available.

OpenRouter (1)

Google

Google Vertex AI (1)

Google Vertex AI
Gemini 3.8 Flash (high) — benchmarks & cost | Benchmark Heaven