Data as of November 9, 2026 · How the score is built
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 | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticRank #14 of 132Percentile 90thWeight 22%12 benchmarksVerified | 67.9 | 12 benchmarks | Verified | |||
| CodingRank #12 of 158Percentile 93rdWeight 20%14 benchmarksVerified | 67.2 | 14 benchmarks | Verified | |||
| ReasoningRank #12 of 157Percentile 93rdWeight 17%5 benchmarksVerified | 73.7 | 5 benchmarks | Verified | |||
| MultimodalRank #9 of 67Percentile 88thWeight 12%4 benchmarksVerified | 75.4 | 4 benchmarks | Verified | |||
| KnowledgeRank #12 of 179Percentile 94thWeight 12%8 benchmarksVerified | 75.4 | 8 benchmarks | Verified | |||
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| Inst. FollowingRank #34 of 157Percentile 79thWeight 5%1 benchmarkVerified | 89.1 | 1 benchmark | Verified | |||
| MathRank Not rankedWeight 5%3 benchmarksVerified | 92.3 | 3 benchmarks | Verified |
67 of 847 tracked benchmark slots have displayable evidence · bars run 0–100
Coverage detailsHow 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.
- Agentic12/12 verified
- Coding14/14 verified
- Reasoning5/5 verified
- Multimodal4/4 verified
- Knowledge8/8 verified
- MultilingualNot measured
- Inst. Following1/1 verified
- Math3/3 verified
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
- Agentic#14/132
- Coding#12/158
- Reasoning#12/157
- Multimodal#9/67
- Knowledge#12/179
- MultilingualNot ranked
- Inst. Following#34/157
- MathNot ranked
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
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| SWE-bench Pro | Score64.6% | Versus best verified row Best verified: Claude Fable 5.2 · 93% | Gap28.4 behind | Weight26% ref. weight | Provider exact |
| DeepSWE | Score72.7% | Versus best verified row Best verified: Muse Wonder Full · 79.0% | Gap6.3 behind | Weight15% ref. weight | Provider exact |
| CursorBench 3.2 | Score67.2% | Versus best verified row Best verified: Claude Fable 5.1 · 73.4% | Gap6.2 behind | Weight10% ref. weight | Benchmark exact |
| FrontierSWE v2 | Score32.2% | Versus best verified row Best verified: Claude Fable 5.2 · 69.9% | Gap37.7 behind | Weight8% ref. weight | Benchmark exact |
| LiveCodeBench (Vals)LiveCodeBench, Vals AI run | Score82.6% | Versus best verified row Best verified: Claude Fable 5.1 · 90.5% | Gap7.9 behind | Weight8% ref. weight | |
| VulcanBench v3 | Score87.0% | Versus best verified row Best verified: Grok 4.5 · 89.9% | Gap2.9 behind | Weight3% ref. weight | Benchmark exact |
| Terminal-Bench 2.1 | Score91.9% | Versus best verified row Best verified: SWE-2 · 92.8% | Gap0.9 behind | WeightScored in Agentic | Provider exact |
| Bug Hunt Bench | Score42.0 fixes | Versus best verified row Best verified: Claude Sonnet 5.5 · 51.3 fixes | Gap9.3 behind | WeightDisplay only | Benchmark exact |
| FrontierCode 1.1 Extended | Score60.6% | Versus best verified row Best verified: GPT-6 Astra · 64.5% | Gap3.9 behind | WeightDisplay only | Provider exact |
| VulcanBench CII v1VulcanBench Coding Intelligence Index v1 | Score86.5% | Versus best verified row Best verified: Claude Opus 5 · 96.4% | Gap9.9 behind | WeightDisplay only | Benchmark exact |
| SWE-bench (Vals)SWE-bench, Vals AI run | Score96.2% | Versus best verified row Best verified: Claude Opus 5 · 97.0% | Gap0.8 behind | WeightDisplay only | Verified |
| CursorBench 4.0 | Score41.7% | Versus best verified row Best verified: Claude Fable 5.2 · 60.0% | Gap18.3 behind | WeightDisplay only | Benchmark exact |
| PostTrainBench v1.1 | Score36.2% | Versus best verified row Best verified: Claude Opus 5.5 · 49.3% | Gap13.1 behind | WeightDisplay only | Benchmark exact |
| FrontierCode 2.0 | Score27.6% | Versus best verified row Best verified: Claude Fable 5.2 · 49.1% | Gap21.5 behind | WeightDisplay only | Benchmark exact |
Agentic12 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| Terminal-Bench 2.1 | Score91.9% | Versus best verified row Best verified: SWE-2 · 92.8% | Gap0.9 behind | Weight8% ref. weight | Provider exact |
| BrowseComp | Score92.2% | Versus best verified row Best verified: Atria Dawn Preview · 92.5% | Gap0.3 behind | Weight8% ref. weight | Provider exact |
| Terminal-Bench 2.1 (Vals)Terminal-Bench 2.1, Vals AI run | Score85.8% | Versus best verified row Best verified: GPT-6 Astra · 87.3% | Gap1.5 behind | Weight4% ref. weight | |
| Terminal-Bench 3.0 | Score34.6% | Versus best verified row Best verified: Claude Opus 5 · 42.7% | Gap8.1 behind | Weight3% ref. weight | Benchmark exact |
| Toolathlon | Score58% | Versus best verified row Best verified: Muse Spark 1.1 · 75.6% | Gap17.6 behind | Weight3% ref. weight | Provider exact |
| Terminal-Bench 4.1 | Score38.50% | Versus best verified row Best verified: Claude Opus 5.5 · 65.80% | Gap27.3 behind | WeightDisplay only | Benchmark exact |
| LifeValLifeVal: everyday life tasks | Score80.0% | Versus best verified row Best verified: GPT-6 Astra · 80.6% | Gap0.6 behind | WeightDisplay only | Provider exact |
| OSWorld 2.0 | Score62.6% | Versus best verified row Best verified: Kimi K3.1 Pro · 79.6% | Gap17 behind | WeightDisplay only | Provider exact |
| CyberGym | Score84.5% | Versus best verified row Best verified: MiMo-V2.6-Flash · 95.1% | Gap10.6 behind | WeightDisplay only | Provider exact |
| ExploitGym | Score33.7% | Versus best verified row Best verified: Gemini 4 Carbon · 53.5% | Gap19.8 behind | WeightDisplay only | Provider exact |
| ApprenticeBenchApprenticeBench: end-to-end computer use, continual learning, and long-horizon agency on a real accounts-payable job | Score26% | Versus best verified row Best verified: Claude Fable 5.1 · 72% | Gap46 behind | WeightDisplay only | Benchmark exact |
| MazeBenchMazeBench: visual spatial reasoning in a 3D open world | Score13% | Versus best verified row Best verified: GPT-6 Astra · 23% | Gap10 behind | WeightDisplay only | Benchmark exact |
Reasoning5 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| ARC-AGI-2Abstraction and Reasoning Corpus for AGI v2 | Score92.5% | Versus best verified row Best verified: GPT-6 Astra · 95% | Gap2.5 behind | Weight15% ref. weight | Benchmark exact |
| ARC-AGI-3Abstraction and Reasoning Corpus for AGI v3 | Score7.8% | Versus best verified row Best verified: Claude Fable 5.2 · 65.7% | Gap57.9 behind | Weight15% ref. weight | Benchmark exact |
| BioBasicsBenchBioBasicsBench: everyday wet-lab science | Score45.6% | Versus best verified row Best verified: GPT-6 Astra · 67.0% | Gap21.4 behind | WeightDisplay only | Provider exact |
| GeneBench-Pro | Score28.7% | Versus best verified row Best verified: GPT-6 Astra · 37.8% | Gap9.1 behind | WeightDisplay only | Provider exact |
| ARC-AGI-1ARC-AGI-1 Semi-Private Evaluation | Score96.50% | Versus best verified row Best verified: Claude Fable 5 · 98.50% | Gap2 behind | WeightDisplay only | Benchmark exact |
Multimodal4 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| MMMU-ProMassive Multi-discipline Multimodal Understanding Pro | Score83% | Versus best verified row Best verified: GPT-6.1 Terra · 85.5% | Gap2.5 behind | Weight30% 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 behind | Weight7% 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 behind | WeightDisplay only | |
| MMMU-Pro w/ PythonMMMU-Pro with Python | Score84.6% | Versus best verified row Best verified: Kimi K3.1 Pro · 90% | Gap5.4 behind | WeightDisplay only | Provider exact |
Knowledge8 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| MMLU-Pro (Vals)MMLU-Pro, Vals AI run | Score89.1% | Versus best verified row Best verified: Claude Fable 5.1 · 92.4% | Gap3.3 behind | Weight6% ref. weight | Verified |
| GPQAGraduate-Level Google-Proof Q&A | Score94.6% | Versus best verified row Best verified: GPT-6 Astra · 96% | Gap1.4 behind | Weight3% ref. weight | Provider exact |
| GPQA Diamond (Vals)GPQA Diamond, Vals AI run | Score95.2% | Versus best verified row Best verified: Gemini 3.1 Pro · 95.5% | Gap0.3 behind | Weight2% ref. weight | |
| GPQA-DGPQA Diamond | Score94.6% | Versus best verified row Best verified: GPT-6 Astra · 96.0% | Gap1.4 behind | WeightDisplay only | Provider exact |
| HLE-Verified | Score54.5% | Versus best verified row Best verified: Gemini 3.8 Flash · 54.9% | Gap0.4 behind | WeightDisplay only | Provider exact |
| LABBench2LABBench2: An Improved Benchmark for AI Systems Performing Biology Research | Score82.1% | Versus best verified row Best verified: Gemini 4 Argon · 88.8% | Gap6.7 behind | WeightDisplay only | Provider exact |
| HealthBench Professional | Score60.5% | Versus best verified row Best verified: Claude Sonnet 5.5 · 69.2% | Gap8.7 behind | WeightDisplay only | Provider exact |
| HealthBench Hard | Score33.1% | Versus best verified row Best verified: Muse Spark · 42.8% | Gap9.7 behind | WeightDisplay only | Provider exact |
Inst. Following1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| Gray Swan IPI (15 attempts)Gray Swan Indirect Prompt Injection, attack success at 15 attempts | Score27.0% | Versus best verified row Best verified: Gemini 4 Carbon · 0.0% | Gap27 behind | WeightDisplay only | Provider exact |
Math3 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)FrontierMath v2 Tiers 1-3 | Score89.000% | Versus best verified row Best verified: GPT-6 Astra · 93.684% | Gap4.7 behind | WeightWeighted 30% | Provider exact |
| FrontierMath v2 (Tier 4)FrontierMath v2 Tier 4 | Score83.000% | Versus best verified row Best verified: GPT-6.1 Sol · 100.000% | Gap17 behind | WeightWeighted 10% | Provider exact |
| FrontierMath (legacy)FrontierMath legacy aggregate | Score89% | Versus best verified row Best verified: GPT-5.6 Sol · 89% | GapBest verified | WeightDisplay only | Provider exact |
External signals20 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| MaxxBenchMaxxBench (mutated DeepSWE tasks) | Score69.1% | Versus best verified row Best verified: Claude Opus 5.5 · 76.4% | Gap7.3 behind | WeightDisplay only | Benchmark exact |
| Clocktower BenchClocktower Bench (Blood on the Clocktower) | Score1591 | Versus best verified row Best verified: GPT-6 Astra · 1859 | Gap268 behind | WeightDisplay only | Benchmark exact |
| ExploitBenchExploitBench v8-bench | Score74% | Versus best verified row Best verified: GPT-6 Astra · 100% | Gap26.5 behind | WeightDisplay only | Provider exact |
| SEC-Bench Pro | Score71.2% | Versus best verified row Best verified: Gemini 4 Carbon · 88.2% | Gap17 behind | WeightDisplay only | Provider exact |
| The Last Ones completionThe Last Ones Cyber Range Completion Rate | Score70% | Versus best verified row Best verified: GPT-5.6 Sol · 70% | GapBest verified | WeightDisplay only | Provider exact |
| ACCR standardAdvanced Cyber Completion Rate — Standard Access | Score1.5% | Versus best verified row Best verified: GPT-6 Astra · 3.5% | Gap2 behind | WeightDisplay only | Provider exact |
| ACCR Daybreak BlueAdvanced Cyber Completion Rate — Daybreak Blue | Score2.0% | Versus best verified row Best verified: GPT-6 Astra · 3.5% | Gap1.5 behind | WeightDisplay only | Provider exact |
| FrontierCyber | Score9.6% | Versus best verified row Best verified: GPT-6 Astra · 38.1% | Gap28.5 behind | WeightDisplay only | Benchmark exact |
| CyScenarioBench successCyScenarioBench Average Success Rate | Score28% | Versus best verified row Best verified: Claude Mythos 5.2 · 76% | Gap48.3 behind | WeightDisplay only | Benchmark exact |
| CyScenarioBench solvedCyScenarioBench Scenarios Ever Solved | Score7% | Versus best verified row Best verified: GPT-6 Astra · 9% | Gap2 behind | WeightDisplay only | Benchmark exact |
| Atomic network attacksAtomic Network Attack Simulation | Score98% | Versus best verified row Best verified: GPT-6 Astra · 100% | Gap2 behind | WeightDisplay only | Benchmark exact |
| Atomic vulnerability researchAtomic Vulnerability Research and Exploitation | Score91% | Versus best verified row Best verified: GPT-6 Astra · 100% | Gap9 behind | WeightDisplay only | Benchmark exact |
| Atomic evasionAtomic Evasion | Score56% | Versus best verified row Best verified: GPT-5.6 Sol · 56% | GapBest verified | WeightDisplay only | Benchmark exact |
| ExploitBench Internal PortExploitBench — Internal Port (June–August 2026) | Score5.5% | Versus best verified row Best verified: GPT-6 Cyber · 42.0% | Gap36.5 behind | WeightDisplay only | Provider exact |
| ExploitGym HoneypotExploitGym Honeypot out-of-scope exploitation rate | Score49.50% | Versus best verified row Best verified: GPT-6 Astra · 0.00% | Gap49.5 behind | WeightDisplay only | Provider exact |
| Chess PuzzlesChess Puzzles (Epoch AI) | Score59.0% | Versus best verified row Best verified: Claude Fable 5.2 · 79.0% | Gap20 behind | WeightDisplay only | Benchmark exact |
| Mystery Game PuzzlesMystery Game Puzzles (Epoch AI) | Score58.0% | Versus best verified row Best verified: Claude Fable 5.2 · 90.0% | Gap32 behind | WeightDisplay only | Benchmark exact |
| Furniture AssemblyFurniture Assembly Benchmark (Epoch AI) | Score56.7% | Versus best verified row Best verified: Claude Fable 5.2 · 90.0% | Gap33.3 behind | WeightDisplay only | Benchmark exact |
| EBR-benchEarthborne Rangers (EBR-bench) | Score44.8% | Versus best verified row Best verified: Claude Fable 5.2 · 90.5% | Gap45.7 behind | WeightDisplay only | Benchmark exact |
| MirrorCodeMirrorCode (Epoch AI and METR) | Score20.0% | Versus best verified row Best verified: Claude Opus 5.5 · 78.9% | Gap58.9 behind | WeightDisplay only | Benchmark exact |
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.
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.
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.
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.
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.
| Host | Input /1M | Output /1M | Cached /1M | Context |
|---|---|---|---|---|
| OpenRouter | $4.00 | $20.00 | $0.40 | 1.05M |
Ask your AI, from current BenchLM data
Where does GPT-5.6 Sol rank right now, and what does it cost?
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.