BenchLeader
OpenAIReasoning model

GPT-6 Astra

Reasoning effort

GPT-6 Astra is an OpenAI proprietary reasoning model, released 2 Sept 2026. Its best configuration (max reasoning effort) ranks #1 of 751 on the BenchLeader Index at 70.2 ±2.9, in the top ten. The ± is the point: 40 other configurations score within that range, so they and this one cannot be told apart on quality alone — price and speed are what separate them. It scores highest in composite (80) and lowest in long context (66). At $20.00 per million tokens blended it is among the most expensive ranked models. Output speed of 54 tokens per second puts it slower than most, with a first answer in 341.9 s. It has been measured at 7 reasoning-effort settings; this summary describes the best-scoring one, and the tabs above switch between them. Last measured 2 Oct 2026.

Blended price
$20.00/M
$10.00 in · $50.00 out
Output speed
54 tok/s
measured by Artificial Analysis
First answer
342 s
first token 3.70 s
Context
1.1M
Full answer
351 s
median, reasoning included
Cost per run
$3.26
one full Intelligence Index run
Released
2 Sept 2026
How it scores by categoryDashed line = average model (50). One step of 15 = one standard deviation.
  1. Overall index70
  2. Reasoning77
  3. Coding75
  4. Agents & tools68
  5. Maths73
  6. Knowledge80
  7. Human preference67
  8. Multimodal66
  9. Long context66
  10. Composite80

Reasoning-effort configurations

The same model behaves differently depending on how much it is allowed to think. Each row is one setting, scored only on the benchmarks that were run at that setting. “Not stated” collects results from publishers that did not say which setting they used; for a reasoning model that is usually its thinking mode, but we do not assume it. Pick a setting here or at the top of the page to see its price, speed and category scores.

EffortIndexRankSpeedFirst answerChat reply costCategories
no reasoning59.2#12431 tok/s3.70 s$0.019Coding 60 · Maths 69 · Reasoning 65
low66.2#3546 tok/s2.82 s$0.019Agents & tools 58 · Coding 61 · Composite 82 · Knowledge 80 · Long context 65 · Maths 72 · Multimodal 68 · Reasoning 76
medium67.9#1945 tok/s6.07 s$0.019Agents & tools 61 · Coding 61 · Composite 86 · Knowledge 81 · Long context 65 · Maths 76 · Multimodal 68 · Reasoning 79
high69.9#545 tok/s58 s$0.019Agents & tools 65 · Coding 71 · Composite 88 · Knowledge 82 · Long context 65 · Maths 76 · Multimodal 70 · Reasoning 79
xhigh69.3#1249 tok/s181 s$0.019Agents & tools 67 · Coding 63 · Composite 90 · Knowledge 81 · Long context 65 · Maths 76 · Multimodal 70 · Reasoning 81
maxbest70.2#154 tok/s342 s$0.019Agents & tools 68 · Coding 75 · Composite 80 · Human preference 67 · Knowledge 80 · Long context 66 · Maths 73 · Multimodal 66 · Reasoning 77
not stated66.4#3031 tok/s3.70 s$0.019Agents & tools 74 · Knowledge 58 · Maths 80 · Reasoning 87

What more thinking costs

Turning the effort up buys index points and multiplies the bill. Here max costs 4.0× low for +4.0 on the index — 2.0 points per doubling of spend. Compare that with other models.

low $0.818max $3.26

How its index has moved

8 Sept 2026 to 3 Oct 2026
616875
  • max
  • high
  • xhigh
  • medium

The index is recomputed from scratch every day, so a line moves when a new benchmark result lands, when a publisher revises a score, or when the models it is normalised against change. Early movement usually means the score is still settling.

Benchmark results

One column per reasoning effort. Rank is among every configuration of every model on that benchmark. Hover a score for the run it came from.

Reasoning

Benchmarkno reasoninglowmediumhighxhighmaxnot statedSourceTrend
GPQA Diamond–––––95.8%#1–Epoch AI Benchmarking Hub
Humanity's Last Exam––––––54.8%#1Scale AI / CAIS
LMArena Hard Prompts–––––1502#30–LMArena
LiveBench Reasoningnot in index–––––92.7%#1–LiveBench
GPQA Diamond (AA)not in index–93.1%#2493.9%#1095.0%#496.3%#196.1%#2–Artificial Analysis
Humanity's Last Exam (AA)not in index–49.2%#2752.7%#1953.1%#1654.6%#1354.7%#12–Artificial Analysis
ARC-AGI-186.0%#8996.5%#2197.5%#1098.5%#198.5%#197.5%#10–ARC Prize
ARC-AGI-259.6%#7885.4%#2692.1%#792.1%#793.3%#395.0%#1–ARC Prize
ARC-AGI-396.7%#698.0%#598.4%#399.9%#198.4%#398.6%#2–ARC Prize
CritPt–26.3%#3629.1%#1928.9%#2231.4%#731.7%#2–Artificial Analysis
MysteryMechanismnot in index–––––53.1%#1–Vals AI
Chess Puzzlesnot in index–––––72.0%#1–Epoch AI Benchmarking Hub
Mystery Game Puzzlesnot in index–––––84.0%#1–Epoch AI Benchmarking Hub
LMCAnot in index–62.2%#1662.9%#1363.8%#964.4%#7–64.1%#8Epoch AI Benchmarking Hub
DTBenchnot in index–94.9%#3397.1%#1296.5%#1797.3%#8–97.1%#12Epoch AI Benchmarking Hub
BTF-3not in index–13.5%#9–––––Epoch AI Benchmarking Hub

Coding

Benchmarkno reasoninglowmediumhighxhighmaxnot statedSourceTrend
SciCode53.5%#5954.0%#5254.2%#5055.4%#3755.7%#3456.5%#25–SciCode
WeirdML–––92.9%#4–93.3%#2–WeirdML
FrontierCode–––––53.3%#4–Cognition
LMArena Coding–––––1543#7–LMArena
LMArena WebDev–––––1788#2–LMArena
LiveBench Codingnot in index–––––80.4%#19–LiveBench
SciCode (AA)not in index–54.0%#6254.2%#6055.4%#4655.7%#4256.5%#33–Artificial Analysis
IOI–––––100.0%#1–Vals AI
Code Migrationnot in index–––––67.7%#3–Vals AI
ProgramBenchnot in index–––––5.5%#3–Vals AI
Vibe Code Bench 1-100not in index–––––27.6%#4–Vals AI
Vibe Code Bench v1.1not in index–––––89.6%#6–Vals AI
SWE Atlas: Codebase QnAnot in index––––59.1%#3––Scale AI SEAL
SWE Atlas: Refactoringnot in index––––59.0%#1––Scale AI SEAL
SWE Atlas: Test Writingnot in index––––50.7%#5––Scale AI SEAL
MirrorCodenot in index–––46.7%#4–––Epoch AI Benchmarking Hub
DeepSWEnot in index–67.0%#2272.8%#873.2%#474.1%#173.2%#4–Epoch AI Benchmarking Hub
ALE-Benchnot in index–––––2951.3#1–Epoch AI Benchmarking Hub
FrontierSWEnot in index–––––65.5%#1–Epoch AI Benchmarking Hub
Terminal-Bench 4.0 (AA)not in index–41.9%#2649.5%#1754.0%#1159.6%#259.1%#5–Artificial Analysis
Terminal-Bench 2.1 (AA)not in index–88.0%#1289.5%#589.9%#389.1%#788.4%#10–Artificial Analysis

Agents & tools

Benchmarkno reasoninglowmediumhighxhighmaxnot statedSourceTrend
Terminal-Bench–50.6%#2954.2%#2357.9%#1657.9%#1658.2%#15–Terminal-Bench
Remote Labor Index––––––20.8%#1Scale AI / CAIS
APEX-Agents––––––64.7%#7Mercor
LMArena Agent–––––12.3#4–LMArena
LiveBench Agentic Codingnot in index–––––57.3%#20–LiveBench
Terminal-Bench 2.1 (Vals)–––––87.3%#2–Vals AI
GDPval (AA)not in index–43.3%#7148.4%#4849.2%#4450.8%#4052.1%#36–Artificial Analysis
τ²-Bench Banking (AA)–32.0%#5935.5%#4540.0%#3043.1%#2141.4%#26–Artificial Analysis
ITBench SRE (AA)not in index–––––48.6%#8–Artificial Analysis
Analyst Agent (AA)not in index–––––51.3%#6–Artificial Analysis
CUA-benchnot in index–––––19.2%#1–Vals AI
CyberBenchnot in index–––––41.1%#38–Vals AI
Finance Agent v2not in index–––––53.5%#27–Vals AI
Harvey's Legal Agent Benchmarknot in index–––––5.4%#28–Vals AI
Legal Research Benchnot in index–––––39.4%#25–Vals AI
SREBenchnot in index–––––56.9%#1–Vals AI
Tax Agent Benchnot in index–––––63.3%#23–Vals AI
Terminal-Bench 4.0 (Vals)not in index–––––59.6%#3–Vals AI
Terminal-Bench Sciencenot in index–––––62.9%#1–Vals AI
Time Horizon Index: KSPnot in index–––––90.5%#1–Vals AI
BALROGnot in index–––––68.3%#1–BALROG
Vending-Bench 2not in index––––––15514.7#1Andon Labs
AutomationBenchnot in index––64.6%#1566.6%#967.2%#668.5%#5–Artificial Analysis
GDP.pdfnot in index––30.4%#731.0%#432.2%#131.0%#4–Artificial Analysis
AA-Briefcasenot in index––1459#301507#231544#181569#14–Artificial Analysis
FACTS Searchnot in index––––––89.9%#1Google

Maths

Benchmarkno reasoninglowmediumhighxhighmaxnot statedSourceTrend
FrontierMath Tiers 1–3–––––93.7%#1–Epoch AI Benchmarking Hub
FrontierMath Tier 482.9%#1187.8%#997.6%#597.6%#297.6%#297.6%#2–Epoch AI Benchmarking Hub
OTIS Mock AIME–––––100.0%#1–Epoch AI Benchmarking Hub
ProofBench––––––99.0%#4Vals AI
LiveBench Mathematicsnot in index–––––96.8%#4–LiveBench
LMArena Maths–––––1490#27–LMArena

Knowledge

Benchmarkno reasoninglowmediumhighxhighmaxnot statedSourceTrend
SimpleQA Verified–––––75.6%#1–Epoch AI Benchmarking Hub
LiveBench Data Analysisnot in index–––––83.0%#1–LiveBench
AA-Omniscience–40.5#1642.2#1043.7#243.4#443.4#5–Artificial Analysis
PRBench Finance––––––47.5%#14Scale AI SEAL
PRBench Legal––––––48.4%#14Scale AI SEAL
BioMysteryBenchnot in index–––––79.3%#3–Vals AI
Excel Modeling Benchmarknot in index–––––71.7%#9–Vals AI
MedCodenot in index–––––48.5%#27–Vals AI
MedScribenot in index–––––87.9%#14–Vals AI
DrugDiscoveryBenchnot in index–––––68.7%#1–Scale AI SEAL
EBR-benchnot in index–––––76.2%#1–Epoch AI Benchmarking Hub
AA-Omniscience: accuracynot in index–59.5%#2160.6%#1861.1%#1461.9%#1362.6%#11–Artificial Analysis
AA-Omniscience: non-hallucinationnot in index–53.1%#9153.5%#9055.2%#8551.7%#9448.7%#110–Artificial Analysis

Instruction following

Benchmarkno reasoninglowmediumhighxhighmaxnot statedSourceTrend
LiveBench Languagenot in index–––––89.4%#4–LiveBench
LiveBench Instruction Followingnot in index–––––75.6%#8–LiveBench
LMArena Instruction Followingnot in index–––––1474#30–LMArena

Human preference

Benchmarkno reasoninglowmediumhighxhighmaxnot statedSourceTrend
LMArena Text–––––1477#29–LMArena
LMArena Creative Writingnot in index–––––1449#39–LMArena
LMArena Multi-turnnot in index–––––1488#23–LMArena
LMArena Longer Queriesnot in index–––––1487#32–LMArena

Multimodal

Benchmarkno reasoninglowmediumhighxhighmaxnot statedSourceTrend
LMArena Vision–––––1280#34–LMArena
MMMU-Pro–84.6%#1885.1%#1186.4%#486.2%#586.9%#2–Artificial Analysis
SAGEnot in index–––––46.4%#36–Vals AI
Blueprint-Bench 2not in index––––––49.7%#1Epoch AI Benchmarking Hub

Long context

Benchmarkno reasoninglowmediumhighxhighmaxnot statedSourceTrend
AA-LCR–80.0%#7079.7%#8080.0%#7080.0%#7080.7%#58–Artificial Analysis
LMArena Documentnot in index–––––1473#17–LMArena
MLCRnot in index–––––35.0%#13–Artificial Analysis

Composite

Benchmarkno reasoninglowmediumhighxhighmaxnot statedSourceTrend
Epoch Capabilities Indexnot in index––––––166.5#2Epoch AI Benchmarking Hub
LiveBench–––––82.2%#4–LiveBench
AA Intelligence Index–45.8#3249.6#2050.9#1552.4#952.7#7–Artificial Analysis
Vals Indexnot in index–––––63.1#6–Vals AI

Where it wins

Benchmarks where this configuration ranks in the top five of every configuration measured.

Where to run it

Every provider serving this model through OpenRouter, with throughput and first-token latency measured on live traffic over the last 30 minutes and each provider’s own price. Purple marks the best in each column.

ProviderSpeedFirst tokenInput $/MOutput $/MContextQuantisation
OpenAI59 tok/s2.33 s$5.00$25.001.1M–
OpenAI46 tok/s4.40 s$20.00$100.001.1M–
OpenAI41 tok/s3.45 s$10.00$50.001.1M–
Azure33 tok/s1.88 s$10.00$50.001.1M–
Azure29 tok/s50 s$11.00$55.001.1M–

What a task costs

Estimates from list price, output speed and time to first answer for the best configuration. “With caching” assumes three-quarters of the input is served from the prompt cache at $1.00 per 1M. Reasoning tokens are not modelled.

WorkloadTokens in / outCostWith cachingTime
Chat reply400 / 300$0.019$0.0165.8 min
Summarise a 30-page report12,000 / 600$0.150$0.0695.9 min
Code edit6,000 / 1,500$0.135$0.0956.2 min
Agentic coding session60,000 / 4,000$0.800$0.3956.9 min
Structured extraction2,000 / 200$0.030$0.0175.8 min

See also

Data as of 4 Oct 2026. Compare these configurations.

Cite as: BenchLeader, “GPT-6 Astra: benchmarks, pricing, speed and rank”, https://www.benchleader.com/models/gpt-6-astra, data as of 4 Oct 2026.