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Gemini 3.5 Flash (High)

deprecated by benchmark source
Google · released 2026-05-19 · 6 offers

Context 1M tokens

Top 3 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
1Googlevia OpenRouter — Price via OpenRouter: OpenRouter's price for Google's endpoint (https://openrouter.ai/google/gemini-3.5-flash/providers), collected 2026-11-03.
2Google Vertex AIdirect — Direct price: Google Vertex AI's own price list (https://cloud.google.com/vertex-ai/generative-ai/pricing), collected 2026-11-03.
3T-Systems LLM Hubdirect — Direct price: T-Systems LLM Hub's own price list (https://docs.llmhub.t-systems.net/models/llms/), collected 2026-11-03.

Composite

6 of 7 inputs · 4 from the model family6 radar axes: DesignArena's two boards share one

50.6

includes −0.7 for its Benchmaxxing signal (from 51.3; why, switch off in Options)

AA Coding v4.xCoding Agent v1.4AA Intelligence (est.)AA AgenticEpoch ECISoftware ECIDesignArenaAA Coding v4.x: percentile 80AA Intelligence (est.): percentile 83AA Agentic: percentile 68Epoch ECI: percentile 55Software ECI: percentile 41DesignArena: percentile 44

AA Coding v4.x 70.1Coding Agent v1.4 —AA Intelligence (est.) 21.1AA Agentic 27.3Epoch ECI 155.0Software ECI 152.7DesignArena 1188/1197

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

Benchmark sheet

46 of 319 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
  • Software ECI · attached
  • DesignArena Web Apps (agentic) · attached
  • DesignArena Full-Stack · attached
Compare this model →

Agentic

  • AA-AnalystAgent7345.0%

    AA-AnalystAgent published 2026-09-10 → · Published board — Tests analyst tasks using agentic Python execution across fourteen domains.

  • APEX-Agents-AA10047.1%

    APEX-Agents-AA published 2026-09-10 → · Published board — Tests professional-service tasks that require agents to produce locally graded deliverables.

  • AA-Briefcase43870 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 v2681,259 Elo

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

  • Harvey LAB-AA3282.1%

    Harvey LAB-AA published 2026-09-10 → · Published board — Tests legal-work deliverables across practice areas on Harvey's private task set. Artificial Analysis' run, graded by one LLM judge against task rubrics — not the same run or scale as Vals AI's HLAB row.

  • ITBench-AA7840.3%

    ITBench-AA published 2026-09-10 → · Published board — Tests root-cause diagnosis from offline Kubernetes incident snapshots.

  • Terminal-Bench Hard (AA)8740.9%

    Terminal-Bench Hard (AA) pinned revision 74221fb → · Published board — Tests a pinned 44-task hard subset of terminal-based work using Terminus 2.

  • Terminal-Bench v2.1 (AA)7978.7%

    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)646.6%

    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.

  • EBR-bench (Earthborne Rangers, Epoch AI)04.8%

    EBR-bench (Earthborne Rangers, Epoch AI) published 2026-09-18 → · Published board — Learning-capability test: models repeatedly play the obscure campaign board game Earthborne Rangers with note-taking, and the score measures whether results improve across playthroughs.

  • Finance Agent v2 (Vals Index v2)7857.9%

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

  • HLAB — Harvey's Legal Agent Benchmark (Vals Index v2)222.5%

    HLAB — Harvey's Legal Agent Benchmark (Vals Index v2) v2 → · Published board — Long-horizon legal work product creation. The index column is Harvey's Legal Agent Benchmark's own published standalone score, reported as accuracy — not comparable with Artificial Analysis' Harvey LAB-AA row.

  • Terminal-Bench 2.1 (Vals Index v2)5774.2%

    Terminal-Bench 2.1 (Vals Index v2) v2 → · Published board — Command-line interface problem solving; the page states that each index column is that benchmark's own published standalone score, so the runner is not asserted here.

  • Vals Index v2 (Vals AI)3053.1%

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

  • AA Agentic Index6827.3

    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

Efficiency

Instruction-following

  • IFBench (AA single-turn)9576.3%

    IFBench (AA single-turn) published 2026-09-10 → · Published board — Tests precise single-turn instructions with deterministic rule checks.

  • LisanBench word chains, v0.2.0 (Lisan al Gaib)824,123

    LisanBench word chains, v0.2.0 (Lisan al Gaib) v0.2.0 → · Published board — A model builds the longest chain of English words it can, each differing from the last by one letter, with no repeats and only dictionary words, from 50 starting words; any broken rule ends the chain.

Knowledge

Long-context

  • GDP.pdf (AA)7519.8%

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

  • AA-LCR v1.17273.3%

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

  • MLCR-AA6618.3%

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

  • Context Arena — MRCR v2 (8-needle)4378.2%

    Context Arena — MRCR v2 (8-needle) 8-needle → · Published board — The model must find and reproduce the right one of eight near-identical earlier replies hidden in a long conversation, averaged over context lengths from 8k to 128k tokens.

Math

  • FrontierMath Tier 4 v2 (Epoch AI)2526.8%

    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.

  • FrontierMath Tiers 1–3 v2 (Epoch AI)3762.8%

    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.

Reasoning

  • Chess Puzzles (Epoch AI)9150.0%

    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.

  • LiveBench 2026-06-253174.6

    LiveBench 2026-06-25 → · Published board — A periodically refreshed suite of objectively graded tasks with separate category scores and an overall category mean.

  • Mystery Game Puzzles (Epoch AI)5532.0%

    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.

Science

Tool-use

  • AutomationBench-AA v1.0.66242.1%

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

  • EnterpriseOps-Gym-AA9650.1%

    EnterpriseOps-Gym-AA published 2026-09-10 → · Published board — Tests enterprise workflows through MCP tools against resettable application environments.

  • τ²-Bench Telecom (AA)9595.3%

    τ²-Bench Telecom (AA) published 2026-09-10 → · Published board — Tests dual-control telecom agents that coordinate tool use with a simulated user.

  • τ³-Banking (AA) v1.0.17232.2%

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

  • MCP Atlas (Scale AI)9383.6%

    MCP Atlas (Scale AI) published 2026-09-21 → · Published board — How reliably a model completes realistic multi-step workflows over real Model Context Protocol servers: discovering the right tool in a noisy tool menu, calling it with correct parameters, recovering from errors and synthesising the results into an accurate final answer.

  • Toolathlon-Verified (HKUST NLP) 2026-06-305067.3%

    Toolathlon-Verified (HKUST NLP) 2026-06-30 → · Published board — An agent completes 108 real-world tool-use tasks across 32 MCP servers and 7 local toolkits, and each task is graded by executing the resulting state against the task's ground truth.

  • Excel Modeling Benchmark (Vals Index v2)4663.5%

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

Vision

Missing coverage · 273 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 2026-11-03 · Intelligence Index v5.0 (est. = estimated from v4, not measured) · Coding Index v4.x frozen 2026-10-27 · Data: Artificial Analysis · Data: Epoch AI (CC BY)

VariantAA CodingAA Intelligence
Gemini 3.5 Flash (High)70.121.1est.
Gemini 3.5 Flash (Medium)—23.0est.
Gemini 3.5 Flash (Minimal)—12.1est.
Token offers by platform · 5 offers (Adjusted $/task)

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

5 offers within the active global filters, 1 more outside them; “price n/a” means the provider serves the model but publishes no direct token price we could collect — no other channel's price is shown in its place.

OpenRouter (2)

Prices via OpenRouter — what OpenRouter charges for each provider's endpoint, not the provider's own direct price. Source: openrouter.ai/google/gemini-3.5-flash/providers, collected 2026-11-03.

Google
Google

Google Vertex AI (2)

Direct prices from Google Vertex AI's own price list. Source: cloud.google.com/vertex-ai/generative-ai/pricing, collected 2026-11-03.

Google Vertex AI
Google Vertex AI

T-Systems LLM Hub (1)

Direct prices from T-Systems LLM Hub's own price list. Source: docs.llmhub.t-systems.net/models/llms/, collected 2026-11-03.

T-Systems LLM Hub
Gemini 3.5 Flash (High) — benchmarks & cost