BenchLeader

GPT-5.6 Sol

Reasoning effort

GPT-5.6 Sol is an OpenAI proprietary reasoning model, released 9 Jul 2026. Its best configuration (max reasoning effort) ranks #16 of 775 on the BenchLeader Index at 68.0 ±3.7, in the top 25. The ± is the point: 70 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 (82) and lowest in knowledge (65). At $8.00 per million tokens blended it is among the most expensive ranked models. Output speed of 84 tokens per second puts it slower than most, with a first answer in 92.7 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 14 Oct 2026.

Blended price
$8.00/M
$4.00 in · $20.00 out
Output speed
84 tok/s
measured by Artificial Analysis
First answer
93 s
first token 2.42 s
Context
1.1M
Full answer
99 s
median, reasoning included
Cost per run
$1.99
one full Intelligence Index run
Released
9 Jul 2026
How it scores by categoryDashed line = average model (50). One step of 15 = one standard deviation.
  1. Overall index68
  2. Reasoning73
  3. Coding66
  4. Agents & tools67
  5. Maths71
  6. Knowledge65
  7. Instruction following71
  8. Multimodal67
  9. Long context67
  10. Composite82

Versions

OpenAI has shipped 3 models under this name. Each is ranked on its own results; a newer version often has fewer results so far, which holds its index nearer the average until more arrive.

ModelReleasedIndexRank
GPT-6.1 Solmax29 Sept 202670.0#7
GPT-6 Solmax22 Sept 202666.6#30
GPT-5.6 Solmaxthis page9 Jul 202668.0#16

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 reasoning53.8#27086 tok/s0.97 s$0.0076Agents & tools 48 · Coding 50 · Composite 61 · Knowledge 62 · Long context 56 · Maths 54 · Multimodal 55 · Reasoning 55
low62.3#8879 tok/s2.26 s$0.0076Agents & tools 67 · Coding 63 · Composite 67 · Instruction following 66 · Knowledge 70 · Long context 64 · Maths 65 · Multimodal 64 · Reasoning 58
medium64.4#5287 tok/s3.69 s$0.0076Agents & tools 73 · Coding 64 · Composite 74 · Instruction following 69 · Knowledge 70 · Long context 65 · Multimodal 65 · Reasoning 65
high66.3#3282 tok/s12 s$0.0076Agents & tools 73 · Coding 70 · Composite 77 · Instruction following 68 · Knowledge 71 · Long context 66 · Multimodal 65 · Reasoning 68
xhigh66.7#2986 tok/s41 s$0.0076Agents & tools 71 · Coding 66 · Composite 79 · Human preference 68 · Instruction following 70 · Knowledge 71 · Long context 66 · Maths 68 · Multimodal 64 · Reasoning 70
maxbest68.0#1684 tok/s93 s$0.0076Agents & tools 67 · Coding 66 · Composite 82 · Instruction following 71 · Knowledge 65 · Long context 67 · Maths 71 · Multimodal 67 · Reasoning 73
not stated55.9#21653 tok/s2.42 s$0.0076Agents & tools 41 · Coding 71 · Composite 59 · Human preference 58 · Reasoning 62

What more thinking costs

Turning the effort up buys index points and multiplies the bill. Here max costs 7.6× low for +5.7 on the index — 1.9 points per doubling of spend. Compare that with other models.

low $0.261max $1.99

How its index has moved

8 Sept 2026 to 19 Oct 2026
586573
  • max
  • xhigh
  • high
  • 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

See all LLM benchmarks. 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 Diamond82.8%#10989.9%#46–––93.5%#16–Epoch AI Benchmarking Hub
SimpleBench––––64.8%#17–64.8%#17SimpleBench
Arena Hard Prompts––––1511#16––Arena (LMArena)
LiveBench 2.0 Reasoningnot in indexeffort not stated––––––91.7%#5LiveBench
LiveBench 1.0 Reasoningnot in index–––––91.7%#5–LiveBench
GPQA Diamond (AA)not in index79.0%#20089.8%#6892.6%#3292.8%#2893.1%#2494.1%#7–Artificial Analysis
Humanity's Last Exam (AA)not in index16.7%#23639.4%#9042.2%#7246.0%#4647.3%#4049.5%#27–Artificial Analysis
GPQA Diamond (Vals)not in index–––––95.2%#2–Vals AI
EnigmaEval–––37.1%#2–––Scale AI SEAL
Kagi LLM Benchmarkeffort not stated––––––67.0%#35Kagi LLM Benchmark
ARC-AGI-1–74.5%#12692.5%#5197.0%#2297.5%#1296.5%#23–ARC Prize
ARC-AGI-2–42.5%#10867.1%#6485.4%#2890.0%#1492.5%#5–ARC Prize
ARC-AGI-3–0.3%#491.1%#372.2%#327.0%#247.8%#23–ARC Prize
CritPt5.1%#15114.9%#9422.9%#5225.7%#4028.6%#2432.3%#2–Artificial Analysis
MysteryMechanismnot in index–––––33.3%#12–Vals AI
Chess Puzzlesnot in index7.0%#12631.0%#65–––59.0%#10–Epoch AI Benchmarking Hub
Mystery Game Puzzlesnot in index33.0%#2726.0%#42–––58.0%#7–Epoch AI Benchmarking Hub
LMCAnot in index52.8%#5355.9%#4356.4%#4256.9%#4058.5%#3758.4%#38–Epoch AI Benchmarking Hub
DTBenchnot in index83.7%#11293.9%#6095.2%#4395.5%#3895.7%#3595.5%#38–Epoch AI Benchmarking Hub
ForecastBenchnot in index––66.5%#2––65.5%#11–ForecastBench
BTF-3not in index––13.7%#713.5%#9–13.7%#7–Epoch AI Benchmarking Hub

Coding

Benchmarkno reasoninglowmediumhighxhighmaxnot statedSourceTrend
SciCode47.1%#12756.4%#3957.4%#2857.8%#2457.1%#3357.1%#33–SciCode
WeirdML–––88.8%#10–87.0%#12–WeirdML
FrontierCodeeffort not stated––––––47.5%#13Cognition
GSO-Bencheffort not stated––––––76.5%#4GSO-Bench
Arena Coding––––1534#16––Arena (LMArena)
Arena WebDev––––1616#25––Arena (LMArena)
LiveBench 2.0 Codingnot in indexeffort not stated––––––89.7%#14LiveBench
LiveBench 1.0 Codingnot in index–––––83.9%#7–LiveBench
SciCode (AA)not in index47.7%#12956.4%#3857.4%#2757.8%#2457.1%#3257.1%#32–Artificial Analysis
LiveCodeBench–––––82.6%#50–Vals AI
IOI–––––91.2%#5–Vals AI
SWE-bench (Vals)not in index–––––96.2%#3–Vals AI
Code Migrationnot in index–––––52.9%#12–Vals AI
ProgramBenchnot in index–––––1.5%#13–Vals AI
Vibe Code Bench 1-100not in index–––––20.0%#11–Vals AI
Vibe Code Bench v1.1not in index–––––80.5%#25–Vals AI
SWE Atlas: Codebase QnAnot in index––––46.0%#7––Scale AI SEAL
SWE Atlas: Test Writingnot in index––––45.9%#7––Scale AI SEAL
MirrorCodenot in index–––20.0%#6–––Epoch AI Benchmarking Hub
Surface Evolver Benchnot in index––––93.1%#3––Epoch AI Benchmarking Hub
DeepSWE v1.1not in index–45.4%#5261.1%#3169.4%#1570.7%#1172.7%#9–Epoch AI Benchmarking Hub
CursorBenchnot in index–24.6%#7431.1%#6235.7%#5137.7%#4341.7%#32–Cursor
ALE-Benchnot in index–––––2176.9#4–Epoch AI Benchmarking Hub
FrontierSWEnot in index–––––32.2%#9–Epoch AI Benchmarking Hub
Terminal-Bench 4.0 (AA)not in index–1.0%#12514.7%#7020.7%#6124.8%#5539.9%#31–Artificial Analysis
Terminal-Bench 2.1 (AA)not in index74.2%#6676.8%#5886.1%#2187.3%#2089.5%#588.0%#12–Artificial Analysis
Startrise Frontendnot in indexeffort not stated––––––66.2%#24Startrise

Agents & tools

Benchmarkno reasoninglowmediumhighxhighmaxnot statedSourceTrend
Terminal-Bench–––––37.3%#50–Terminal-Bench
OSWorld-Verified 2.0–––––27.3%#4–OSWorld
Arena Agent––––6.6#10––Arena (LMArena)
LiveBench Long Agenticnot in indexeffort not stated––––––48.5%#14LiveBench
LiveBench Browsernot in indexeffort not stated––––––88.0%#7LiveBench
LiveBench Agentic Codingnot in index–––––56.2%#29–LiveBench
Terminal-Bench Hard–60.6%#762.9%#262.1%#561.4%#665.9%#1–Artificial Analysis
τ²-Bench Telecom (AA)not in index–76.0%#13381.0%#11883.3%#10984.8%#9685.1%#93–Artificial Analysis
Terminal-Bench 2.1 (Vals)–––––85.8%#3–Vals AI
HiL-Bencheffort not stated––––––32.3%#23Scale AI SEAL
τ³-bench Bankingnot in index––––46.9%#4––τ²-bench
GDPval-AA v2.1not in index37.1%#11340.2%#10246.1%#6950.2%#5153.4%#4055.4%#30–Artificial Analysis
τ³-Banking (AA)19.6%#9829.1%#6936.5%#4436.7%#4238.1%#3844.3%#17–Artificial Analysis
ITBench SRE (AA)not in index–––––56.2%#1–Artificial Analysis
Analyst Agent (AA)not in index–––––47.5%#14–Artificial Analysis
CUA-benchnot in index–––––8.3%#5–Vals AI
CyberBenchnot in index–––––76.3%#4–Vals AI
Finance Agent v2not in index–––––53.8%#30–Vals AI
Legal Research Benchnot in index–––––48.1%#10–Vals AI
SkillsBenchnot in index–––––54.1%#15–Vals AI
SREBenchnot in index–––––30.5%#6–Vals AI
Tax Agent Benchnot in index–––––31.1%#23–Vals AI
Terminal-Bench 4.0 (Vals)not in index–––––37.9%#13–Vals AI
Terminal-Bench Sciencenot in index–––––20.0%#11–Vals AI
Time Horizon Index: KSPnot in index–––––23.8%#5–Vals AI
Web Search Indexnot in index–––––43.6%#4–Vals AI
Arena Searchnot in index––––1258#1––Arena (LMArena)
BALROGnot in index–––––60.0%#3–BALROG
PostTrainBenchnot in index–––––36.2%#2–Epoch AI Benchmarking Hub
GBAEvalnot in indexeffort not stated––––––52.6%#7Epoch AI Benchmarking Hub
Vending-Bench 2not in indexeffort not stated––––––9619.4#8Andon Labs
AutomationBenchnot in index22.7%#12941.0%#9451.3%#7455.3%#5655.3%#5660.1%#35–Artificial Analysis
GDP.pdf (AA)not in index15.2%#9621.0%#5526.2%#2227.8%#1427.6%#1527.2%#16–Artificial Analysis
EnterpriseOps-Gymnot in index–––––42.9%#20–Artificial Analysis
Terminal-Bench-Science 0.1 (AA)not in index–––––22.4%#15–Artificial Analysis
FACTS Searchnot in indexeffort not stated––––––88.5%#2Google

Maths

Benchmarkno reasoninglowxhighmaxSourceTrend
FrontierMath Tiers 1–3–––89.1%#6Epoch AI Benchmarking Hub
FrontierMath Tier 4–––82.9%#11Epoch AI Benchmarking Hub
OTIS Mock AIME68.9%#14095.6%#43–100.0%#1Epoch AI Benchmarking Hub
ProofBench–––83.0%#12Vals AI
LiveBench Mathematicsnot in index–––96.2%#10LiveBench
Arena Maths––1496#21–Arena (LMArena)

Knowledge

Benchmarkno reasoninglowmediumhighxhighmaxnot statedSourceTrend
SimpleQA Verified–––––69.7%#8–Epoch AI Benchmarking Hub
LiveBench 2.0 Data Analysisnot in indexeffort not stated––––––79.8%#10LiveBench
LiveBench Researchnot in indexeffort not stated––––––54.5%#41LiveBench
LiveBench 1.0 Data Analysisnot in index–––––79.8%#10–LiveBench
AA-Omniscience1.1#12418.9#7219.4#7020.4#6721.0#6222.0#59–Artificial Analysis
MMLU-Pro–––––89.1%#16–Vals AI
LegalBench–––––87.0%#9–Vals AI
CorpFin–––––64.4%#38–Vals AI
TaxEval–––––74.8%#28–Vals AI
PRBench Finance–––––50.5%#10–Scale AI SEAL
PRBench Legal–––––50.5%#9–Scale AI SEAL
BioMysteryBenchnot in index–––––71.1%#11–Vals AI
Excel Modeling Benchmarknot in index–––––72.3%#7–Vals AI
MedCodenot in index–––––44.0%#43–Vals AI
MedScribenot in index–––––85.2%#30–Vals AI
Public Benefits Benchnot in index–––––66.5%#17–Vals AI
DrugDiscoveryBenchnot in index–––––56.5%#3–Scale AI SEAL
EBR-benchnot in index–––––44.8%#8–Epoch AI Benchmarking Hub
GDP.pdfnot in index–––––30.7%#530.7%#5Epoch AI Benchmarking Hub
AA-Omniscience: accuracynot in index48.7%#6957.2%#3057.8%#2958.4%#2758.8%#2659.4%#23–Artificial Analysis
AA-Omniscience: non-hallucinationnot in index7.2%#48510.6%#4049.2%#4308.8%#4438.1%#4657.8%#472–Artificial Analysis

Instruction following

BenchmarklowmediumhighxhighmaxSourceTrend
LiveBench Languagenot in index––––87.7%#8LiveBench
LiveBench Instruction Followingnot in index––––71.8%#22LiveBench
IFBench66.5%#9469.6%#7369.2%#7471.0%#6272.7%#46Artificial Analysis
Arena Instruction Followingnot in index–––1491#13–Arena (LMArena)

Human preference

Benchmarkxhighnot statedSourceTrend
Arena Text1485#21–Arena (LMArena)
EQ-Bench 4effort not stated–1250#9EQ-Bench
Arena Creative Writingnot in index1469#20–Arena (LMArena)
Arena Multi-turnnot in index1488#23–Arena (LMArena)
Arena Longer Queriesnot in index1499#18–Arena (LMArena)

Multimodal

Benchmarkno reasoninglowmediumhighxhighmaxnot statedSourceTrend
Arena Vision––––1281#35––Arena (LMArena)
LiveBench Designnot in indexeffort not stated––––––81.6%#13LiveBench
MMMU-Pro71.9%#13381.0%#4181.4%#3881.8%#3682.7%#3083.4%#25–Artificial Analysis
MMMU-Pro (Vals)not in index–––––88.8%#6–Vals AI
MortgageTaxnot in index–––––67.3%#27–Vals AI
SAGEnot in index–––––52.6%#6–Vals AI
Blueprint-Bench 2not in indexeffort not stated––––––33.6%#10Epoch AI Benchmarking Hub

Long context

Benchmarkno reasoninglowmediumhighxhighmaxSourceTrend
AA-LCR62.3%#26878.0%#12080.3%#7081.7%#5182.3%#4084.0%#11Artificial Analysis
Arena Documentnot in index––––1489#8–Arena (LMArena)
MLCRnot in index–12.8%#5315.0%#4617.2%#3818.3%#3326.1%#22Artificial Analysis

Composite

Benchmarkno reasoninglowmediumhighxhighmaxnot statedSourceTrend
Epoch Capabilities Indexnot in indexeffort not stated––––––162.8#11Epoch AI Benchmarking Hub
LiveBench 2.0effort not stated––––––76.3%#14LiveBench
LiveBench 1.0not in index–––––81.0%#11–LiveBench
AA Intelligence Index v4.3.228.3#13433.5#10639.2#7042.4#5044.0#4347.0#28–Artificial Analysis
Vals Multimodal Indexnot in index–––––72.6%#4–Vals AI

Safety & honesty

Benchmarknot statedSourceTrend
FORTRESSnot in index20.1%#40Scale AI SEAL

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
OpenAI104 tok/s3.21 s$8.00$40.001.1M–
OpenAI87 tok/s2.05 s$2.00$10.001.1M–
Amazon Bedrock84 tok/s3.81 s$4.40$22.001.1M–
Azure48 tok/s4.32 s$4.00$20.001.1M–
OpenAI47 tok/s1.95 s$4.00$20.001.1M–

Price history

Listed price per 1M tokens over time, as recorded by OpenRouter for the provider with the longest history.

$0.00$8.25$16.50$24.75$33.00Jul 26Aug 26Sept 26Oct 26
input outputnow $4.00 in · $20.00 out

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 $0.400 per 1M. Reasoning tokens are not modelled.

WorkloadTokens in / outCostWith cachingTime
Chat reply400 / 300$0.0076$0.00651.6 min
Summarize a 30-page report12,000 / 600$0.060$0.0281.7 min
Code edit6,000 / 1,500$0.054$0.0381.8 min
Agentic coding session60,000 / 4,000$0.320$0.1582.3 min
Structured extraction2,000 / 200$0.012$0.00661.6 min

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See also

Data as of 21 Oct 2026. Compare these configurations.

Cite as: BenchLeader, “GPT-5.6 Sol: benchmarks, pricing, speed and rank”, https://www.benchleader.com/models/gpt-5-6-sol, data as of 21 Oct 2026.