Gemini 3.8 Flash (high)
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.
| # | Provider | Platform | Raw input $/1M | Raw output $/1M | Adjusted $/task |
|---|---|---|---|---|---|
| 1 | OpenRouter | $0.750 | $3.75 | ||
| 2 | Google Vertex AI | Google Vertex AI | $0.750 | $3.75 |
Composite
6 of 7 inputs · 3 from the model family6 radar axes: DesignArena's two boards share one81.8
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 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.
- Epoch ECI · attached
- DesignArena Web Apps (agentic) · attached
- DesignArena Full-Stack · attached
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
CursorBench 4.0 cost per task (Cursor) v4.0no percentile$4.70
CursorBench 4.0 cost per task (Cursor) v4.0 ↗ · Published board — Cursor's published USD cost per task for each CursorBench 4.0 model-effort configuration.
ApprenticeBench API cost per task (NeoCognition)54$1.13
ApprenticeBench API cost per task (NeoCognition) published 2026-09-14 ↗ · Claude Code — ApprenticeBench's published USD cost per task for each API-board model-harness-effort configuration on the 100-task accounts-payable job.
Vals Index v2 cost per test (Vals AI)37$5.39
Vals Index v2 cost per test (Vals AI) v2 ↗ · Published board — Vals AI's published USD cost per test for each model on the Vals Index v2.
FrontierCode 1.1 Main cost per rollout (Cognition) v1.1no percentile$2.60
FrontierCode 1.1 Main cost per rollout (Cognition) v1.1 ↗ · chisel — Cognition's published mean USD spend per rollout for each FrontierCode 1.1 Main model and reasoning effort.
Knowledge
Humanity's Last Exam (AA text-only)9747.8%
Humanity's Last Exam (AA text-only) published 2026-09-10 ↗ · Published board — Tests expert-level knowledge on AA's text-only Humanity's Last Exam subset.
AA-Omniscience Index9729.6
AA-Omniscience Index published 2026-09-10 ↗ · Published board — Tests factual reliability while rewarding correct answers and penalizing hallucinations.
Legal Research Bench (Vals Index v2)5338.9%
Legal Research Bench (Vals Index v2) v2 ↗ · Published board — Case and statute research with citation-backed answers.
SimpleQA Verified (run by Epoch AI)8969.7%
SimpleQA Verified (run by Epoch AI) published 2026-09-16 ↗ · Published board — Short fact-seeking questions answered without tools, testing whether a model knows a fact rather than guessing; Google DeepMind's cleaned version of OpenAI's SimpleQA, run by Epoch AI.
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
CritPt (AA)9218.3%
CritPt (AA) published 2026-09-10 ↗ · Published board — Tests research-level physics reasoning with Python, symbolic and numerical answers.
GPQA Diamond (AA)10095.3%
GPQA Diamond (AA) published 2026-09-10 ↗ · Published board — Tests graduate-level biology, physics and chemistry knowledge on the Diamond subset.
GPQA Diamond (Epoch AI run)9895.4%
GPQA Diamond (Epoch AI run) published 2026-09-18 ↗ · Published board — Epoch AI's own inspect-ai runs of GPQA Diamond, the 198-question graduate-level science multiple-choice set.
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
MMMU Pro (AA)9985.6%
MMMU Pro (AA) published 2026-09-10 ↗ · Published board — Tests multimodal understanding using challenging ten-option questions.
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)
| Variant | AA Coding | AA Intelligence |
|---|---|---|
| Gemini 3.8 Flash (high) | 76.3 | 41.2 |
| Gemini 3.8 Flash (medium) | 74.1 | 40.0 |
| Gemini 3.8 Flash (low) | 73.5 | 33.8 |
SUBSCRIPTION PLAN
GitHub Copilot
Current usage-based billing · model token cost is converted to AI Credits at 1 credit = $0.01.
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)
| globalgoogle-vertex/global | $0.750 raw in $/1M | $3.75 raw out $/1M |
Google Vertex AI (1)
| Google Vertex AI | global | $0.750 raw in $/1M | $3.75 raw out $/1M |