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Data

Data as of November 9, 2026 · How the score is built

Superseded.OpenAI has newer models in this line:GPT-6 AstraGPT-6 Sol
SupersededProprietaryReasoning

Released Jul 9, 20261.05M context

GPT-5.6 Sol

Decision readingGPT-5.6 Sol scores 74.4 out of 100 and ranks #14 of 214. This profile shows 67 source-displayable benchmark rows; its strongest eligible category is Multimodal & Grounded at #9. API pricing is $4 input and $20 output per million tokens, with cached input at $0.4.

Released Jul 9, 2026 — see all recent releases

Decision snapshot

Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.

Capability

74.4/100

field median 51.1#14 of 214 ranked models

Public

#14of 214

Verified #10 of 77

Price

$4input / $20 output

input median $0.60cached $0.40 · blended $12

Speed

94tok/s

field median 92 tok/sFirst token 100.66 s

Context

1.05Mtokens

field median 256,000Maximum output length is tracked separately

Strongest published evidence

Multimodal & Grounded ranks #9. Particularly strong for screenshots, documents, charts, and grounded multimodal workflows.

Validate before choosing

67 published rows leave some tracked benchmark slots empty. Instruction Following is its lowest eligible category at #34.

Source-linked · 67 displayable benchmark rows

Category score record

Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.

Category scores, ranks, weighting, benchmark coverage, and evidence status
CategoryScore
AgenticRank #14 of 132Percentile 90thWeight 22%12 benchmarksVerified
67.9
CodingRank #12 of 158Percentile 93rdWeight 20%14 benchmarksVerified
67.2
ReasoningRank #12 of 157Percentile 93rdWeight 17%5 benchmarksVerified
73.7
MultimodalRank #9 of 67Percentile 88thWeight 12%4 benchmarksVerified
75.4
KnowledgeRank #12 of 179Percentile 94thWeight 12%8 benchmarksVerified
75.4
MultilingualWeight 7%0 benchmarksNot measured
Not measured
Inst. FollowingRank #34 of 157Percentile 79thWeight 5%1 benchmarkVerified
89.1
MathRank Not rankedWeight 5%3 benchmarksVerified
92.3

67 of 847 tracked benchmark slots have displayable evidence · bars run 0–100

Coverage details

How much of this is verified

Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.

  1. Agentic12/12 verified
  2. Coding14/14 verified
  3. Reasoning5/5 verified
  4. Multimodal4/4 verified
  5. Knowledge8/8 verified
  6. MultilingualNot measured
  7. Inst. Following1/1 verified
  8. Math3/3 verified
Verified sourceProvisionalNot measured

Capability shape

Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.

GPT-5.6 Sol category percentile values

  • Agentic90th percentile
  • Coding93rd percentile
  • Reasoning93rd percentile
  • Multimodal88th percentile
  • Knowledge94th percentile
  • MultilingualNot eligible
  • Instruction following79th percentile
  • MathNot eligible

The dashed outline is median of 6 nearest peers.

Eligible category ranks

  1. Agentic#14/132
  2. Coding#12/158
  3. Reasoning#12/157
  4. Multimodal#9/67
  5. Knowledge#12/179
  6. MultilingualNot ranked
  7. Inst. Following#34/157
  8. MathNot ranked
Top decileTop quartileMid-fieldNot eligible

Benchmark ledger

Coding opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.

Coding14 rows
Coding benchmark values, best verified comparison, weight, and source status
SWE-bench ProScore64.6%Versus best verified row

Best verified: Claude Fable 5.2 · 93%

Gap28.4 behindWeight26% ref. weight
Provider exact
DeepSWEScore72.7%Versus best verified row

Best verified: Muse Wonder Full · 79.0%

Gap6.3 behindWeight15% ref. weight
Provider exact
CursorBench 3.2Score67.2%Versus best verified row

Best verified: Claude Fable 5.1 · 73.4%

Gap6.2 behindWeight10% ref. weight
FrontierSWE v2Score32.2%Versus best verified row

Best verified: Claude Fable 5.2 · 69.9%

Gap37.7 behindWeight8% ref. weight
LiveCodeBench (Vals)LiveCodeBench, Vals AI runScore82.6%Versus best verified row

Best verified: Claude Fable 5.1 · 90.5%

Gap7.9 behindWeight8% ref. weight
VulcanBench v3Score87.0%Versus best verified row

Best verified: Grok 4.5 · 89.9%

Gap2.9 behindWeight3% ref. weight
Terminal-Bench 2.1Score91.9%Versus best verified row

Best verified: SWE-2 · 92.8%

Gap0.9 behindWeightScored in Agentic
Provider exact
Bug Hunt BenchScore42.0 fixesVersus best verified row

Best verified: Claude Sonnet 5.5 · 51.3 fixes

Gap9.3 behindWeightDisplay only
FrontierCode 1.1 ExtendedScore60.6%Versus best verified row

Best verified: GPT-6 Astra · 64.5%

Gap3.9 behindWeightDisplay only
VulcanBench CII v1VulcanBench Coding Intelligence Index v1Score86.5%Versus best verified row

Best verified: Claude Opus 5 · 96.4%

Gap9.9 behindWeightDisplay only
SWE-bench (Vals)SWE-bench, Vals AI runScore96.2%Versus best verified row

Best verified: Claude Opus 5 · 97.0%

Gap0.8 behindWeightDisplay only
CursorBench 4.0Score41.7%Versus best verified row

Best verified: Claude Fable 5.2 · 60.0%

Gap18.3 behindWeightDisplay only
PostTrainBench v1.1Score36.2%Versus best verified row

Best verified: Claude Opus 5.5 · 49.3%

Gap13.1 behindWeightDisplay only
FrontierCode 2.0Score27.6%Versus best verified row

Best verified: Claude Fable 5.2 · 49.1%

Gap21.5 behindWeightDisplay only
Agentic12 rows
Agentic benchmark values, best verified comparison, weight, and source status
Terminal-Bench 2.1Score91.9%Versus best verified row

Best verified: SWE-2 · 92.8%

Gap0.9 behindWeight8% ref. weight
Provider exact
BrowseCompScore92.2%Versus best verified row

Best verified: Atria Dawn Preview · 92.5%

Gap0.3 behindWeight8% ref. weight
Provider exact
Terminal-Bench 2.1 (Vals)Terminal-Bench 2.1, Vals AI runScore85.8%Versus best verified row

Best verified: GPT-6 Astra · 87.3%

Gap1.5 behindWeight4% ref. weight
Terminal-Bench 3.0Score34.6%Versus best verified row

Best verified: Claude Opus 5 · 42.7%

Gap8.1 behindWeight3% ref. weight
ToolathlonScore58%Versus best verified row

Best verified: Muse Spark 1.1 · 75.6%

Gap17.6 behindWeight3% ref. weight
Provider exact
Terminal-Bench 4.1Score38.50%Versus best verified row

Best verified: Claude Opus 5.5 · 65.80%

Gap27.3 behindWeightDisplay only
LifeValLifeVal: everyday life tasksScore80.0%Versus best verified row

Best verified: GPT-6 Astra · 80.6%

Gap0.6 behindWeightDisplay only
Provider exact
OSWorld 2.0Score62.6%Versus best verified row

Best verified: Kimi K3.1 Pro · 79.6%

Gap17 behindWeightDisplay only
Provider exact
CyberGymScore84.5%Versus best verified row

Best verified: MiMo-V2.6-Flash · 95.1%

Gap10.6 behindWeightDisplay only
Provider exact
ExploitGymScore33.7%Versus best verified row

Best verified: Gemini 4 Carbon · 53.5%

Gap19.8 behindWeightDisplay only
Provider exact
ApprenticeBenchApprenticeBench: end-to-end computer use, continual learning, and long-horizon agency on a real accounts-payable jobScore26%Versus best verified row

Best verified: Claude Fable 5.1 · 72%

Gap46 behindWeightDisplay only
MazeBenchMazeBench: visual spatial reasoning in a 3D open worldScore13%Versus best verified row

Best verified: GPT-6 Astra · 23%

Gap10 behindWeightDisplay only
Reasoning5 rows
Reasoning benchmark values, best verified comparison, weight, and source status
ARC-AGI-2Abstraction and Reasoning Corpus for AGI v2Score92.5%Versus best verified row

Best verified: GPT-6 Astra · 95%

Gap2.5 behindWeight15% ref. weight
ARC-AGI-3Abstraction and Reasoning Corpus for AGI v3Score7.8%Versus best verified row

Best verified: Claude Fable 5.2 · 65.7%

Gap57.9 behindWeight15% ref. weight
BioBasicsBenchBioBasicsBench: everyday wet-lab scienceScore45.6%Versus best verified row

Best verified: GPT-6 Astra · 67.0%

Gap21.4 behindWeightDisplay only
GeneBench-ProScore28.7%Versus best verified row

Best verified: GPT-6 Astra · 37.8%

Gap9.1 behindWeightDisplay only
ARC-AGI-1ARC-AGI-1 Semi-Private EvaluationScore96.50%Versus best verified row

Best verified: Claude Fable 5 · 98.50%

Gap2 behindWeightDisplay only
Multimodal4 rows
Multimodal benchmark values, best verified comparison, weight, and source status
MMMU-ProMassive Multi-discipline Multimodal Understanding ProScore83%Versus best verified row

Best verified: GPT-6.1 Terra · 85.5%

Gap2.5 behindWeight30% ref. weight
Provider exact
ParseBenchParseBench: document parsing for AI agents (LlamaIndex leaderboard)Score74.40%Versus best verified row

Best verified: Claude Opus 5.5 · 78.80%

Gap4.4 behindWeight7% ref. weight
ExtractBench (LlamaIndex)ExtractBench: schema-guided enterprise document extraction (LlamaIndex leaderboard)Score93.77%Versus best verified row

Best verified: GPT-6.1 Sol · 95.31%

Gap1.5 behindWeightDisplay only
MMMU-Pro w/ PythonMMMU-Pro with PythonScore84.6%Versus best verified row

Best verified: Kimi K3.1 Pro · 90%

Gap5.4 behindWeightDisplay only
Provider exact
Knowledge8 rows
Knowledge benchmark values, best verified comparison, weight, and source status
MMLU-Pro (Vals)MMLU-Pro, Vals AI runScore89.1%Versus best verified row

Best verified: Claude Fable 5.1 · 92.4%

Gap3.3 behindWeight6% ref. weight
GPQAGraduate-Level Google-Proof Q&AScore94.6%Versus best verified row

Best verified: GPT-6 Astra · 96%

Gap1.4 behindWeight3% ref. weight
Provider exact
GPQA Diamond (Vals)GPQA Diamond, Vals AI runScore95.2%Versus best verified row

Best verified: Gemini 3.1 Pro · 95.5%

Gap0.3 behindWeight2% ref. weight
GPQA-DGPQA DiamondScore94.6%Versus best verified row

Best verified: GPT-6 Astra · 96.0%

Gap1.4 behindWeightDisplay only
Provider exact
HLE-VerifiedScore54.5%Versus best verified row

Best verified: Gemini 3.8 Flash · 54.9%

Gap0.4 behindWeightDisplay only
LABBench2LABBench2: An Improved Benchmark for AI Systems Performing Biology ResearchScore82.1%Versus best verified row

Best verified: Gemini 4 Argon · 88.8%

Gap6.7 behindWeightDisplay only
HealthBench ProfessionalScore60.5%Versus best verified row

Best verified: Claude Sonnet 5.5 · 69.2%

Gap8.7 behindWeightDisplay only
Provider exact
HealthBench HardScore33.1%Versus best verified row

Best verified: Muse Spark · 42.8%

Gap9.7 behindWeightDisplay only
Inst. Following1 row
Inst. Following benchmark values, best verified comparison, weight, and source status
Gray Swan IPI (15 attempts)Gray Swan Indirect Prompt Injection, attack success at 15 attemptsScore27.0%Versus best verified row

Best verified: Gemini 4 Carbon · 0.0%

Gap27 behindWeightDisplay only
Math3 rows
Math benchmark values, best verified comparison, weight, and source status
FrontierMath v2 (Tiers 1-3)FrontierMath v2 Tiers 1-3Score89.000%Versus best verified row

Best verified: GPT-6 Astra · 93.684%

Gap4.7 behindWeightWeighted 30%
Provider exact
FrontierMath v2 (Tier 4)FrontierMath v2 Tier 4Score83.000%Versus best verified row

Best verified: GPT-6.1 Sol · 100.000%

Gap17 behindWeightWeighted 10%
Provider exact
FrontierMath (legacy)FrontierMath legacy aggregateScore89%Versus best verified row

Best verified: GPT-5.6 Sol · 89%

GapBest verifiedWeightDisplay only
Provider exact
External signals20 rows
External signals benchmark values, best verified comparison, weight, and source status
MaxxBenchMaxxBench (mutated DeepSWE tasks)Score69.1%Versus best verified row

Best verified: Claude Opus 5.5 · 76.4%

Gap7.3 behindWeightDisplay only
Clocktower BenchClocktower Bench (Blood on the Clocktower)Score1591Versus best verified row

Best verified: GPT-6 Astra · 1859

Gap268 behindWeightDisplay only
ExploitBenchExploitBench v8-benchScore74%Versus best verified row

Best verified: GPT-6 Astra · 100%

Gap26.5 behindWeightDisplay only
Provider exact
SEC-Bench ProScore71.2%Versus best verified row

Best verified: Gemini 4 Carbon · 88.2%

Gap17 behindWeightDisplay only
Provider exact
The Last Ones completionThe Last Ones Cyber Range Completion RateScore70%Versus best verified row

Best verified: GPT-5.6 Sol · 70%

GapBest verifiedWeightDisplay only
Provider exact
ACCR standardAdvanced Cyber Completion Rate — Standard AccessScore1.5%Versus best verified row

Best verified: GPT-6 Astra · 3.5%

Gap2 behindWeightDisplay only
ACCR Daybreak BlueAdvanced Cyber Completion Rate — Daybreak BlueScore2.0%Versus best verified row

Best verified: GPT-6 Astra · 3.5%

Gap1.5 behindWeightDisplay only
FrontierCyberScore9.6%Versus best verified row

Best verified: GPT-6 Astra · 38.1%

Gap28.5 behindWeightDisplay only
CyScenarioBench successCyScenarioBench Average Success RateScore28%Versus best verified row

Best verified: Claude Mythos 5.2 · 76%

Gap48.3 behindWeightDisplay only
CyScenarioBench solvedCyScenarioBench Scenarios Ever SolvedScore7%Versus best verified row

Best verified: GPT-6 Astra · 9%

Gap2 behindWeightDisplay only
Atomic network attacksAtomic Network Attack SimulationScore98%Versus best verified row

Best verified: GPT-6 Astra · 100%

Gap2 behindWeightDisplay only
Atomic vulnerability researchAtomic Vulnerability Research and ExploitationScore91%Versus best verified row

Best verified: GPT-6 Astra · 100%

Gap9 behindWeightDisplay only
Atomic evasionAtomic EvasionScore56%Versus best verified row

Best verified: GPT-5.6 Sol · 56%

GapBest verifiedWeightDisplay only
ExploitBench Internal PortExploitBench — Internal Port (June–August 2026)Score5.5%Versus best verified row

Best verified: GPT-6 Cyber · 42.0%

Gap36.5 behindWeightDisplay only
Provider exact
ExploitGym HoneypotExploitGym Honeypot out-of-scope exploitation rateScore49.50%Versus best verified row

Best verified: GPT-6 Astra · 0.00%

Gap49.5 behindWeightDisplay only
Chess PuzzlesChess Puzzles (Epoch AI)Score59.0%Versus best verified row

Best verified: Claude Fable 5.2 · 79.0%

Gap20 behindWeightDisplay only
Mystery Game PuzzlesMystery Game Puzzles (Epoch AI)Score58.0%Versus best verified row

Best verified: Claude Fable 5.2 · 90.0%

Gap32 behindWeightDisplay only
Furniture AssemblyFurniture Assembly Benchmark (Epoch AI)Score56.7%Versus best verified row

Best verified: Claude Fable 5.2 · 90.0%

Gap33.3 behindWeightDisplay only
EBR-benchEarthborne Rangers (EBR-bench)Score44.8%Versus best verified row

Best verified: Claude Fable 5.2 · 90.5%

Gap45.7 behindWeightDisplay only
MirrorCodeMirrorCode (Epoch AI and METR)Score20.0%Versus best verified row

Best verified: Claude Opus 5.5 · 78.9%

Gap58.9 behindWeightDisplay only

What it costs to get this score

Published API price against the public score. The x-axis uses a log scale; the dashed path marks models that are not beaten by a cheaper, higher-scoring option. Price uses average of published input and output rates.

Current modelExplore all models

GPT-5.6 Sol · 74.4 score · $12 blended per million tokens

The chart opens on the current model. Scroll horizontally to inspect the full price axis.

405060708090100$0.10$0.50$1$5$10$25$50$100↘ frontierGPT-5.6 Sol

Horizontal: blended price per million tokens, log scale · Vertical: public score

Published price history

This chart appears only when at least two dated first-party price records exist for the exact model. It shows rate changes, not an inferred cost trend.

018362026-072026-09Output priceInput price

2 dated first-party price records. The newest record lists $20 per million tokens.

Lineage

The sequence follows explicit supersedes links. Each score is estimated for that model; a relative can inform a sparse estimate but never sets a floor, so a newer release can score below an earlier one. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.

  1. Mar 5, 2026

    GPT-5.4

    Score 66.1 · $2.5 / $15

  2. Apr 23, 2026

    GPT-5.5

    Score 68.4 · $5 / $30

  3. Jul 9, 2026 · you are here

    GPT-5.6 Sol

    Score 74.4 · $4 / $20

  4. Sep 3, 2026

    GPT-6 Astra

    Score 85.2 · $10 / $50

  5. Sep 22, 2026

    GPT-6 Sol

    Score 77.7 · $2 / $10

Spec sheet

Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.

API model ID
gpt-5.6-solOpenAI model catalog
Context window
1.05MOpenAI model catalog
Maximum output
Not sourced yet
Knowledge cutoff
Not sourced yet
Input modalities
text, imageOpenAI model catalog
Output modalities
textOpenAI model catalog
Parameters
Not disclosed by the provider
Availability
OpenAI Responses APIOpenAI model catalog
Cloud regions
Not tracked yet
Lifecycle
activeOpenAI model catalog
API capabilities
Tool calling, structured outputs, and batch support are not tracked yet
Prompt caching
Published at $0.40 per million cached input tokensOpenAI pricing
Self-host
Weights are not published
Rate limits
Not tracked yet

How to read this profile

The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.

GPT-5.6 Sol ranks #14 of 214 on the public leaderboard with a score of 74.36/100. Its source-verified position is #10 of 77.

GPT-5.6 Sol is a proprietary model with a 1.05M context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.

Generally available as gpt-5.6-sol. Approved Daybreak Blue users call the same model through the gpt-daybreak-blue alias with modified cyber safeguards; BenchLM does not treat Blue as a separate model.

Official exact-value snapshot from OpenAI's July 9, 2026 GPT-5.6 GA launch page and system card, supplemented by Anthropic's Fable 5.1 system card for FrontierSWE v2, Google's Gemini 3.8 comparison for HLE-Verified and LABBench2, and Bug Hunt Bench's max-effort run. We map the highest published GPT-5.6 Sol setting for each benchmark, using Sol Ultra where the launch table provides it and regular Sol otherwise. The added cross-provider rows and cyber evidence remain display-only. The Daybreak Blue ACCR row measures approved-request completion under modified safeguards, not model capability.

GPT-5.6 Sol sits in the GPT-5.6 family with GPT-5.6 Terra, GPT-5.6 Luna, GPT-5.6 Cyber. Its explicit predecessor is GPT-5.5. 67 of 847 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.

Its strongest eligible category is Multimodal & Grounded at #9, while its lowest eligible position is Instruction Following at #34. particularly strong for screenshots, documents, charts, and grounded multimodal workflows.

Last updated November 9, 2026. Runtime fields remain blank until a sourced snapshot exists.

Deployment options

Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.

Where to run GPT-5.6 Sol by API

OpenRouter is the one host we track that publishes a per-token price for it. Prices read from each host’s own pages on October 20, 2026.

Inference hosts for GPT-5.6 Sol with input, output and cached-input prices per million tokens.
HostInput /1MOutput /1MCached /1MContext
OpenRouter$4.00$20.00$0.401.05M
Compare inference hosts

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Where does GPT-5.6 Sol rank right now, and what does it cost?

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Questions

How does GPT-5.6 Sol perform overall in AI benchmarks?

GPT-5.6 Sol ranks #14 out of 214 models on the public BenchAlign leaderboard, with a score of 74.36/100. Its evidence status is Supported, and this profile shows 67 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.

Is GPT-5.6 Sol good for knowledge and understanding?

GPT-5.6 Sol ranks #12 out of 179 eligible models for knowledge and understanding, with a public category score of 75.4/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Is GPT-5.6 Sol good for coding and programming?

GPT-5.6 Sol ranks #12 out of 158 eligible models for coding and programming, with a public category score of 67.2/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Is GPT-5.6 Sol good for mathematics?

GPT-5.6 Sol has source-displayable benchmark coverage for mathematics, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.

Is GPT-5.6 Sol good for reasoning and logic?

GPT-5.6 Sol ranks #12 out of 157 eligible models for reasoning and logic, with a public category score of 73.7/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Is GPT-5.6 Sol good for agentic tool use and computer tasks?

GPT-5.6 Sol ranks #14 out of 132 eligible models for agentic tool use and computer tasks, with a public category score of 67.9/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Is GPT-5.6 Sol good for multimodal and grounded tasks?

GPT-5.6 Sol ranks #9 out of 67 eligible models for multimodal and grounded tasks, with a public category score of 75.4/100. That places it in the current top ten for this category. Check the underlying rows before treating the aggregate as a workload guarantee.

Is GPT-5.6 Sol good for instruction following?

GPT-5.6 Sol ranks #34 out of 157 eligible models for instruction following, with a public category score of 89.1/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.

Which sibling models are related to GPT-5.6 Sol?

GPT-5.6 Sol belongs to the GPT-5.6 family. Related tracked variants include GPT-5.6 Terra, GPT-5.6 Luna, GPT-5.6 Cyber. A sibling link indicates shared lineage or a documented configuration relationship; it does not mean the variants have identical pricing, context limits, benchmark evidence, or deployment behavior. Compare before switching.

Does GPT-5.6 Sol have full benchmark coverage on BenchLM?

No. GPT-5.6 Sol currently has 89 source-displayable rows. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.

What is the context window size of GPT-5.6 Sol?

GPT-5.6 Sol has a documented context window of 1.05M. That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.

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Compare GPT-5.6 Sol with every tracked model1019 comparisons