GPT-5.6 Terra (Max)
Context 1M tokens
Top 5 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 | Price channel | Raw input $/1M | Raw output $/1M | Adjusted $/task |
|---|---|---|---|---|---|
| 1 | AWS Bedrockdirect — Direct price: AWS Bedrock's own price list (https://aws.amazon.com/bedrock/pricing/), collected 2026-11-03. | direct — Direct price: AWS Bedrock's own price list (https://aws.amazon.com/bedrock/pricing/), collected 2026-11-03. | $2.20 | $13.20 | |
| 2 | Azure AI Foundrydirect — Direct price: Azure AI Foundry's own price list (https://prices.azure.com/api/retail/prices), collected 2026-11-03. | direct — Direct price: Azure AI Foundry's own price list (https://prices.azure.com/api/retail/prices), collected 2026-11-03. | $2.00 | $12.00 | |
| 3 | Azurevia OpenRouter — Price via OpenRouter: OpenRouter's price for Azure's endpoint (https://openrouter.ai/openai/gpt-5.6-terra/providers), collected 2026-11-03. | via OpenRouter — Price via OpenRouter: OpenRouter's price for Azure's endpoint (https://openrouter.ai/openai/gpt-5.6-terra/providers), collected 2026-11-03. | $2.00 | $12.00 | |
| 4 | Amazon Bedrockvia OpenRouter — Price via OpenRouter: OpenRouter's price for Amazon Bedrock's endpoint (https://openrouter.ai/openai/gpt-5.6-terra/providers), collected 2026-11-03. | via OpenRouter — Price via OpenRouter: OpenRouter's price for Amazon Bedrock's endpoint (https://openrouter.ai/openai/gpt-5.6-terra/providers), collected 2026-11-03. | $2.20 | $13.20 | |
| 5 | T-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. | direct — Direct price: T-Systems LLM Hub's own price list (https://docs.llmhub.t-systems.net/models/llms/), collected 2026-11-03. | $3.95 | $17.74 |
Composite
7 of 7 inputs · 4 from the model family6 radar axes: DesignArena's two boards share one62.9
AA Coding v4.x 76.7Coding Agent v1.4 60.4AA Intelligence (est.) 32.2AA Agentic —Epoch ECI 160.1Software ECI 159.6DesignArena 1105/1061
Radar: percentile among all models measured on each input; a gap means not measured.
Benchmark sheet
57 of 319 registered benchmark versions · bars show the percentile among all models measured on each benchmark.
7 of 58 values are OpenAI's own claims (†), not independent measurements; a matching independent result replaces a claim as soon as one exists.
- Epoch ECI · attached
- Software ECI · attached
- DesignArena Web Apps (agentic) · attached
- DesignArena Full-Stack · attached
Agentic
APEX-Agents-AA9338.9%
APEX-Agents-AA published 2026-09-10 → · Published board — Tests professional-service tasks that require agents to produce locally graded deliverables.
AA-Briefcase761,330 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 v2851,477 Elo
GDPval-AA v2 v2 → · Published board — Tests professional knowledge-work deliverables across occupations using AA's Stirrup harness.
Harvey LAB-AA4985.2%
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-AA9751.0%
ITBench-AA published 2026-09-10 → · Published board — Tests root-cause diagnosis from offline Kubernetes incident snapshots.
Terminal-Bench Hard (AA)9757.6%
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)9588.0%
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)8935.4%
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)2521.0%
ApprenticeBench API (NeoCognition) published 2026-09-14 → · Codex — 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.
ApprenticeBench CUA (NeoCognition)4516.0%
ApprenticeBench CUA (NeoCognition) published 2026-09-14 → · Codex — A computer-use agent operates the Odoo ERP through its screens across 100 sequential accounts-payable tasks with diminishing mentoring.
Terminal-Bench v4.0developer's claim21.5%† self-reported by the developer
Terminal-Bench v4.0 → · Codex — A benchmark to measure and evolve with the frontier of agent work, whose homepage leaderboard reports resolution rate on Terminal-Bench 4.0 tasks.
Finance Agent v2 (Vals Index v2)5454.4%
Finance Agent v2 (Vals Index v2) v2 → · Published board — Multi-step financial reasoning tasks.
HLAB — Harvey's Legal Agent Benchmark (Vals Index v2)50.8%
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)6977.5%
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)6259.6%
Vals Index v2 (Vals AI) v2 → · Published board — GDP-weighted average of agentic model performance across finance, coding and legal tasks.
Coding
Artificial Analysis Coding Agent Index v1.47360.4%
Artificial Analysis Coding Agent Index v1.4 v1.4 → · Codex — The retained AA Coding Agent Index measures coding-agent systems using the earlier three-component implementation.
SciCode (AA subproblems) v1.0.17755.0%
SciCode (AA subproblems) v1.0.1 → · Published board — Tests scientific Python programming with scientist-annotated background information.
CursorBench 4.0 (Cursor)developer's claim41.3%† self-reported by the developer
CursorBench 4.0 (Cursor) v4.0 → · Published board — Agent evaluation on ambiguous, multi-file tasks drawn from real Cursor sessions.
DeepSWE (Datacurve, via Epoch AI)7969.6%
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.
FrontierCode 1.1 Main (Cognition)developer's claim41.3%† self-reported by the developer
FrontierCode 1.1 Main (Cognition) v1.1 → · Codex — Whether a maintainer would merge the agent's pull request, on tasks crafted by open-source maintainers and graded with tests, rubrics and verifiers.
Code Migration (Vals Index v2 subset)8048.2%
Code Migration (Vals Index v2 subset) v2 → · Published board — Porting projects to another language, including COBOL modernization.
Vibe Code Bench (Vals Index v2)4574.6%
Vibe Code Bench (Vals Index v2) v2 → · Published board — End-to-end app-building tasks.
VulcanBench Frontier v49189.1%
VulcanBench Frontier v4 v4 → · Codex — 23 behavioural-reconstruction tasks: the model must repair a replacement implementation of a legacy program until hidden tests confirm it reproduces the program’s real drift from its written spec; the combined score weights functional correctness 50%, lint/complexity 8.5%, security 8.5% and judged Code quality 33%.
FrontierCode 1.1 Extended (Cognition)developer's claim55.8%† self-reported by the developer
FrontierCode 1.1 Extended (Cognition) v1.1 → · Codex — Whether a maintainer would merge the agent's pull request, on the full 150-task FrontierCode set crafted by open-source maintainers and graded with tests, rubrics and verifiers.
AA Coding Index v4.x · frozen 2026-10-279576.7
AA Coding Index v4.x · frozen 2026-10-27 → · Published board — Artificial Analysis withdrew the Coding Index with Intelligence Index v5.0 (2026-10-28). These are the last v4.x values, frozen with their own date (2026-10-27).
Efficiency
ApprenticeBench API cost per task (NeoCognition)75$1.13
ApprenticeBench API cost per task (NeoCognition) published 2026-09-14 → · Codex — ApprenticeBench's published USD cost per task for each API-board model-harness-effort configuration on the 100-task accounts-payable job.
ApprenticeBench CUA cost per task (NeoCognition)45$6.15
ApprenticeBench CUA cost per task (NeoCognition) published 2026-09-14 → · Codex — ApprenticeBench's published USD cost per task for each CUA model-harness-effort configuration on the 100-task accounts-payable job.
CursorBench 4.0 cost per task (Cursor)developer's claim$5.14† self-reported by the developer
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.
FrontierCode 1.1 Main cost per rollout (Cognition)developer's claim$1.87† self-reported by the developer
FrontierCode 1.1 Main cost per rollout (Cognition) v1.1 → · Codex — Cognition's published mean USD spend per rollout for each FrontierCode 1.1 Main model and reasoning effort.
Vals Index v2 cost per test (Vals AI)41$5.10
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 Extended cost per rollout (Cognition)developer's claim$1.57† self-reported by the developer
FrontierCode 1.1 Extended cost per rollout (Cognition) v1.1 → · Codex — Cognition's published mean USD spend per rollout for each FrontierCode 1.1 Extended model and reasoning effort.
Instruction-following
IFBench (AA single-turn)8771.2%
IFBench (AA single-turn) published 2026-09-10 → · Published board — Tests precise single-turn instructions with deterministic rule checks.
Knowledge
Humanity's Last Exam (AA text-only)9342.9%
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 Index800.05
AA-Omniscience Index published 2026-09-10 → · Published board — Tests factual reliability while rewarding correct answers and penalizing hallucinations.
SimpleQA Verified (run by Epoch AI)3343.2%
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.
Legal Research Bench (Vals Index v2)6441.3%
Legal Research Bench (Vals Index v2) v2 → · Published board — Case and statute research with citation-backed answers.
Long-context
GDP.pdf (AA)8824.0%
GDP.pdf (AA) published 2026-09-10 → · Published board — Tests professional reasoning over long PDFs with AA document preparation and grading.
AA-LCR v1.19683.0%
AA-LCR v1.1 v1.1 → · Published board — Tests reasoning across multiple long documents with corrected answer keys and grading.
MLCR-AA8331.7%
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)8592.6%
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)6870.7%
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)8686.0%
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)9354.0%
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 2026-06-255777.9
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)6935.0%
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.
Safety/Alignment
BullshitBench V1 (clear pushback)6734.5%
BullshitBench V1 (clear pushback) published 2026-09-10 → · Published board — Whether a model rejects the broken premise of 55 deliberately nonsensical prompts (V1 question set) instead of answering them confidently.
BullshitBench V2 (clear pushback)7354.0%
BullshitBench V2 (clear pushback) published 2026-09-10 → · Published board — Whether a model rejects the broken premise of 100 deliberately nonsensical prompts (V2 question set) instead of answering them confidently.
Science
CritPt (AA)9830.0%
CritPt (AA) published 2026-09-10 → · Published board — Tests research-level physics reasoning with Python, symbolic and numerical answers.
GPQA Diamond (AA)9492.5%
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)8593.3%
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 v1.0.68859.6%
AutomationBench-AA v1.0.6 → · Published board — Tests multi-app SaaS workflows through REST tools on a held-out AutomationBench split.
EnterpriseOps-Gym-AA3838.5%
EnterpriseOps-Gym-AA published 2026-09-10 → · Published board — Tests enterprise workflows through MCP tools against resettable application environments.
τ²-Bench Telecom (AA)7986.3%
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) v1.0.18640.2%
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)5966.2%
Excel Modeling Benchmark (Vals Index v2) v2 → · Published board — Building and editing financial models in spreadsheets.
Vision
MMMU Pro (AA)8780.7%
MMMU Pro (AA) published 2026-09-10 → · Published board — Tests multimodal understanding using challenging ten-option questions.
Chartography 100-tasks6434.0%
Chartography 100-tasks → · Published board — Can a model read the charts professionals rely on — Kaplan-Meier curves, candlesticks, contour maps, Sankey diagrams, Bode plots, wind roses — from the image alone, to an expert-set precision?
Furniture Assembly Benchmark (Epoch AI)7054.2%
Furniture Assembly Benchmark (Epoch AI) published 2026-09-26 → · Published board — Spatial-reasoning test by Epoch AI: given a photo of a partly or fully assembled IKEA product and its official manual, the model must say whether the build is correct and, if not, name the step with the mistake.
Unusual results
1 threshold-crossing signal flagged · 44 eligible benchmark families
How flags are calculated
Heuristic screen, not statistical significance: benchmark families are correlated and source uncertainty is unknown. Peer evidence needs ≥ 20 independently measured matched configurations from ≥ 10 distinct model families. A flag needs a directed population z-score of magnitude ≥ 1.5 and a gap of ≥ 1.5 from the leave-one-benchmark-family-out mean z in the same direction, over ≥ 5 other benchmark families.
- unusually strong CritPt (AA) published 2026-09-10
Why
Observed: 0.3 fractionPeer mean 0.04412 · peer sd 0.08179 · n 537 · families 370Directed z 3.129 · baseline z 0.789 · gap 2.34 · profile n 430.3 fractionmeasuredobserved 2026-09-10artificialanalysis.ai ↗Evidence
Axis: CritPt (AA) · published 2026-09-10 · Published boardExact value:0.3fractionObserved: 2026-09-10T21:47:16.627Z · publication date: not recordedObservation id:aa:bcf8db0a-3bb6-4d82-9516-0f57370c85a6:critpt
Missing coverage · 262 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)
| Variant | AA Coding | AA Intelligence |
|---|---|---|
| GPT-5.6 Terra (Max) | 76.7 | 32.2est. |
| GPT-5.6 Terra (Xhigh) | 70.6 | 27.8est. |
| GPT-5.6 Terra (High) | 67.1 | 24.0est. |
| GPT-5.6 Terra (Medium) | 64.7 | 18.4est. |
| GPT-5.6 Terra (Low) | 58.1 | 17.3est. |
| GPT-5.6 Terra (Non-reasoning) | 52.3 | 9.8est. |
SUBSCRIPTION PLAN
GitHub Copilot
Current usage-based billing · model token cost is converted to AI Credits at 1 credit = $0.01.
Token offers by platform · 8 offers (Adjusted $/task)
Click any underlined price to see how it is estimated and where each input comes from. How we calculate adjusted cost.
8 offers within the active global filters, 2 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.
AWS Bedrock (1)
Direct prices from AWS Bedrock's own price list. Source: aws.amazon.com/bedrock/pricing/, collected 2026-11-03.
| AWS Bedrock | us-east-1 | $2.20 raw in $/1M | $13.20 raw out $/1M |
Azure AI Foundry (2)
Direct prices from Azure AI Foundry's own price list. Source: prices.azure.com/api/retail/prices, collected 2026-11-03.
| Azure AI Foundry | global | $2.00 raw in $/1M | $12.00 raw out $/1M | |
| Azure AI Foundry | euEU | $2.20 raw in $/1M | $13.20 raw out $/1M |
OpenRouter (4)
Prices via OpenRouter — what OpenRouter charges for each provider's endpoint, not the provider's own direct price. Source: openrouter.ai/openai/gpt-5.6-terra/providers, collected 2026-11-03.
| Azureconditional | globalazure | $2.00 raw in $/1M | $12.00 raw out $/1M | |
| Azureconditional | globalazure/us | $2.20 raw in $/1M | $13.20 raw out $/1M | |
| Amazon Bedrockconditional | globalamazon-bedrock/us-east-1 | $2.20 raw in $/1M | $13.20 raw out $/1M | |
| Azureconditional | euazure/euEU | $2.20 raw in $/1M | $13.20 raw out $/1M |
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 | euEU | $3.95 raw in $/1M | $17.74 raw out $/1M |