Data as of October 20, 2026 · How the score is built
GPT-5.5
Decision readingGPT-5.5 scores 69.2 out of 100 and ranks #20 of 227. This profile shows 48 source-displayable benchmark rows; its strongest eligible category is Coding at #14. API pricing is $5 input and $30 output per million tokens, with cached input at $0.5.
Released Apr 23, 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
69.2/100
field median 49.8#20 of 227 ranked models
Public
#20of 227
Verified #15 of 87
Price
$5input / $30 output
input median $0.72cached $0.50 · blended $17.50
Speed
98tok/s
field median 93.5 tok/sFirst token 37.05 s
Context
1Mtokens
field median 256,000Maximum output length is tracked separately
Strongest published evidence
Coding ranks #14. Particularly well-suited for software development and code generation tasks.
Validate before choosing
48 published rows leave some tracked benchmark slots empty. Multimodal & Grounded is its lowest eligible category at #23.
Source-linked · 48 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 #23 of 131Percentile 83rdWeight 22%15 benchmarksVerified | 62.7 | 15 benchmarks | Verified | |||
| CodingRank #14 of 152Percentile 91stWeight 20%10 benchmarksVerified | 62.0 | 10 benchmarks | Verified | |||
| ReasoningRank #17 of 27Percentile 38thWeight 17%5 benchmarksVerified | 70.3 | 5 benchmarks | Verified | |||
| MultimodalRank #23 of 55Percentile 59thWeight 12%5 benchmarksVerified | 75.5 | 5 benchmarks | Verified | |||
| KnowledgeRank #15 of 187Percentile 92ndWeight 12%6 benchmarksVerified | 72.1 | 6 benchmarks | Verified | |||
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| Inst. FollowingWeight 5%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MathRank Not rankedWeight 5%3 benchmarksVerified | 91.2 | 3 benchmarks | Verified |
48 of 726 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.
- Agentic15/15 verified
- Coding10/10 verified
- Reasoning5/5 verified
- Multimodal5/5 verified
- Knowledge6/6 verified
- MultilingualNot measured
- Inst. FollowingNot measured
- 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-5.5 category percentile values
- Agentic83rd percentile
- Coding91st percentile
- Reasoning38th percentile
- Multimodal59th percentile
- Knowledge92nd percentile
- MultilingualNot eligible
- Instruction followingNot eligible
- MathNot eligible
The dashed outline is median of 6 nearest peers.
Eligible category ranks
- Agentic#23/131
- Coding#14/152
- Reasoning#17/27
- Multimodal#23/55
- Knowledge#15/187
- MultilingualNot ranked
- Inst. FollowingNot ranked
- 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.
Coding10 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| SWE-bench Pro | Score58.6% | Versus best verified row Best verified: Claude Opus 5.5 · 89.9% | Gap31.3 behind | Weight26% ref. weight | Provider exact |
| CursorBench 3.2 | Score58.4% | Versus best verified row Best verified: Claude Fable 5.1 · 73.4% | Gap15 behind | Weight10% ref. weight | Benchmark exact |
| LiveCodeBench (Vals)LiveCodeBench, Vals AI run | Score85.3% | Versus best verified row Best verified: Claude Fable 5.1 · 90.5% | Gap5.2 behind | Weight8% ref. weight | |
| FrontierCode 1.1 Main | Score43.0% | Versus best verified row Best verified: Claude Opus 5.5 · 54.4% | Gap11.4 behind | Weight4% ref. weight | Benchmark exact |
| Terminal-Bench 2.0 | Score82.0% | Versus best verified row Best verified: GPT-5.5 · 82.0% | GapBest verified | WeightScored in Agentic | Benchmark exact |
| Vibe Code BenchVibe Code Bench v1.1 | Score69.85% | Versus best verified row Best verified: Gemini 4 Argon · 91.90% | Gap22.1 behind | WeightDisplay only | Benchmark exact |
| React Native Evals | Score84.7% | Versus best verified row Best verified: Composer 2 · 96.1% | Gap11.4 behind | WeightDisplay only | Benchmark exact |
| cursorBench31 | Score59.2% | Versus best verified row Best verified: Claude Fable 5 · 70.6% | Gap11.4 behind | WeightDisplay only | Benchmark exact |
| SWE-bench (Vals)SWE-bench, Vals AI run | Score82.6% | Versus best verified row Best verified: Claude Opus 5 · 97.0% | Gap14.4 behind | WeightDisplay only | Verified |
| PostTrainBench v1.1 | Score27.2% | Versus best verified row Best verified: Claude Opus 5.5 · 49.3% | Gap22.1 behind | WeightDisplay only | Benchmark exact |
Agentic15 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| BrowseComp | Score84.4% | Versus best verified row Best verified: Atria Dawn Preview · 92.5% | Gap8.1 behind | Weight8% ref. weight | Provider exact |
| Terminal-Bench 2.0 | Score82% | Versus best verified row Best verified: GPT-5.5 · 82% | GapBest verified | Weight7% ref. weight | Benchmark exact |
| OSWorld-Verified | Score78.7% | Versus best verified row Best verified: Qwen3.8 Max · 86.1% | Gap7.4 behind | Weight7% ref. weight | Provider exact |
| JobBench | Score42.7% | Versus best verified row Best verified: Muse Wonder Full · 75.3% | Gap32.6 behind | Weight6% ref. weight | Benchmark exact |
| MCP Atlas | Score75.3% | Versus best verified row Best verified: Muse Spark Open · 89.2% | Gap13.9 behind | Weight4% ref. weight | Provider exact |
| Terminal-Bench 2.1 (Vals)Terminal-Bench 2.1, Vals AI run | Score76.4% | Versus best verified row Best verified: GPT-6 Astra · 87.3% | Gap10.9 behind | Weight4% ref. weight | |
| Toolathlon | Score55.6% | Versus best verified row Best verified: Muse Spark 1.1 · 75.6% | Gap20 behind | Weight3% ref. weight | Provider exact |
| CyberGym | Score81.8% | Versus best verified row Best verified: MiMo-V2.6-Flash · 95.1% | Gap13.3 behind | WeightDisplay only | Provider exact |
| τ²-bench resultsτ²-Bench Tool-Agent-User Evaluation | Score98% | Versus best verified row Best verified: GPT-5.4 · 98.9% | Gap0.9 behind | WeightDisplay only | Provider exact |
| Gert LabsGert Labs Composite Game Benchmark | Score72.93% | Versus best verified row Best verified: Claude Opus 4.8 · 72.97% | Gap0 behind | WeightDisplay only | Benchmark exact |
| ResearchClawBench | Score17.0% | Versus best verified row Best verified: Claude Opus 4.8 · 21.1% | Gap4.1 behind | WeightDisplay only | Benchmark exact |
| OSWorld 2.0 | Score13.0% | Versus best verified row Best verified: GPT-6 Astra · 72.6% | Gap59.6 behind | WeightDisplay only | Benchmark exact |
| ExploitGym | Score13.4% | Versus best verified row Best verified: GPT-6 Astra · 42.4% | Gap29 behind | WeightDisplay only | Benchmark exact |
| ApprenticeBenchApprenticeBench: end-to-end computer use, continual learning, and long-horizon agency on a real accounts-payable job | Score20% | Versus best verified row Best verified: Claude Fable 5.1 · 72% | Gap52 behind | WeightDisplay only | Benchmark exact |
| MazeBenchMazeBench: visual spatial reasoning in a 3D open world | Score0% | Versus best verified row Best verified: GPT-6 Astra · 23% | Gap23 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 | Score85% | Versus best verified row Best verified: GPT-6 Astra · 95% | Gap10 behind | WeightWeighted 25% | Provider exact |
| ARC-AGI-3Abstraction and Reasoning Corpus for AGI v3 | Score0.4% | Versus best verified row Best verified: GPT-6 Astra · 62.7% | Gap62.3 behind | WeightWeighted 15% | Benchmark exact |
| MRCR v2 64K-128KOpenAI MRCR v2 8-needle 64K-128K | Score83.1% | Versus best verified row Best verified: Gemini 3.7 Flash · 97% | Gap13.9 behind | WeightDisplay only | Provider exact |
| MRCR v2 128K-256KOpenAI MRCR v2 8-needle 128K-256K | Score87.5% | Versus best verified row Best verified: GPT-5.5 · 87.5% | GapBest verified | WeightDisplay only | Provider exact |
| ARC-AGI-1ARC-AGI-1 Semi-Private Evaluation | Score95.00% | Versus best verified row Best verified: Claude Fable 5 · 98.50% | Gap3.5 behind | WeightDisplay only | Benchmark exact |
Multimodal5 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| MMMU-ProMassive Multi-discipline Multimodal Understanding Pro | Score81.2% | Versus best verified row Best verified: Gemini 3.5 Flash · 83.6% | Gap2.4 behind | WeightWeighted 40% | Provider exact |
| OfficeQA Pro | Score54.1% | Versus best verified row Best verified: Claude Opus 5.5 · 67.7% | Gap13.6 behind | WeightWeighted 25% | Provider exact |
| ExtractBench (LlamaIndex)ExtractBench: schema-guided enterprise document extraction (LlamaIndex leaderboard) | Score93.57% | Versus best verified row Best verified: Claude Sonnet 5.5 · 94.68% | Gap1.1 behind | WeightDisplay only | |
| ParseBenchParseBench: document parsing for AI agents (LlamaIndex leaderboard) | Score67.76% | Versus best verified row Best verified: Claude Opus 5.5 · 79.85% | Gap12.1 behind | WeightDisplay only | |
| MMMU-Pro w/ PythonMMMU-Pro with Python | Score83.2% | Versus best verified row Best verified: GPT-5.6 Sol · 84.6% | Gap1.4 behind | WeightDisplay only | Provider exact |
Knowledge6 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| HLEHumanity's Last Exam | Score52.2% | Versus best verified row Best verified: Claude Fable 5.1 · 65% | Gap12.8 behind | Weight44% ref. weight | Provider exact |
| HLE w/o toolsHumanity's Last Exam without tools | Score41.4% | Versus best verified row Best verified: Claude Opus 5.5 · 64.4% | Gap23 behind | Weight7% ref. weight | Provider exact |
| MMLU-Pro (Vals)MMLU-Pro, Vals AI run | Score88.1% | Versus best verified row Best verified: Claude Fable 5.1 · 92.4% | Gap4.3 behind | Weight6% ref. weight | Verified |
| GPQAGraduate-Level Google-Proof Q&A | Score93.6% | Versus best verified row Best verified: GPT-6 Astra · 96% | Gap2.4 behind | Weight3% ref. weight | Provider exact |
| GPQA Diamond (Vals)GPQA Diamond, Vals AI run | Score93.2% | Versus best verified row Best verified: Gemini 3.1 Pro · 95.5% | Gap2.3 behind | Weight2% ref. weight | |
| GPQA-DGPQA Diamond | Score93.6% | Versus best verified row Best verified: GPT-6 Astra · 96.0% | Gap2.4 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 | Score85.263% | Versus best verified row Best verified: GPT-6 Astra · 93.684% | Gap8.4 behind | WeightWeighted 30% | Benchmark exact |
| FrontierMath v2 (Tier 4)FrontierMath v2 Tier 4 | Score72.500% | Versus best verified row Best verified: GPT-6.1 Sol · 100.000% | Gap27.5 behind | WeightWeighted 10% | Benchmark exact |
| FrontierMath (legacy)FrontierMath legacy aggregate | Score51.7% | Versus best verified row Best verified: GPT-5.6 Sol · 89% | Gap37.3 behind | WeightDisplay only | Provider exact |
External signals4 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| SEC-Bench Pro | Score45.8% | Versus best verified row Best verified: GPT-6 Astra · 85.4% | Gap39.6 behind | WeightDisplay only | Provider exact |
| Atomic network attacksAtomic Network Attack Simulation | Score100% | Versus best verified row Best verified: GPT-6 Astra · 100% | GapBest verified | WeightDisplay only | Benchmark exact |
| Atomic vulnerability researchAtomic Vulnerability Research and Exploitation | Score92% | Versus best verified row Best verified: GPT-6 Astra · 100% | Gap8 behind | WeightDisplay only | Benchmark exact |
| Atomic evasionAtomic Evasion | Score54% | Versus best verified row Best verified: GPT-5.6 Sol · 56% | Gap2 behind | WeightDisplay only | Benchmark exact |
Bars run 0–100; the dark tick marks the best source-verified value
All 48 rowsWhat 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-5.5 · 69.2 score · $17.50 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
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.
Base entry
GPT-5.5 Pro · 73.5Spec 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-5.5OpenAI pricing
- Context window
- 1MOpenAI pricing
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- Not sourced yet
- Output modalities
- Not sourced yet
- Parameters
- Not disclosed by the provider
- Availability
- Not sourced yet
- Cloud regions
- Not tracked yet
- Lifecycle
- Superseded
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Published at $0.50 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-5.5 ranks #20 of 227 on the public leaderboard with a score of 69.22/100. Its source-verified position is #15 of 87.
GPT-5.5 is a proprietary model with a 1M context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.
Official exact-value snapshot from OpenAI's April 23, 2026 GPT-5.5 launch post, supplemented by CursorBench v3.1 and CursorBench v3.2's highest published GPT-5.5 setting. BenchLM maps only directly comparable public benchmark rows from the launch tables. JobBench uses the Codex CLI GPT-5.5 row reported by the JobBench paper. ExploitGym is normalized from 120 successful exploits out of 898 tasks in the public paper.
GPT-5.5 sits in the GPT-5.5 family with GPT-5.5 Pro. Its explicit predecessor is GPT-5.4. 48 of 726 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Coding at #14, while its lowest eligible position is Multimodal & Grounded at #23. particularly well-suited for software development and code generation tasks.
Last updated October 20, 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-5.5 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 | $5.00 | $30.00 | $0.50 | 1.05M |
Questions
How does GPT-5.5 perform overall in AI benchmarks?
GPT-5.5 ranks #20 out of 227 models on the public BenchAlign leaderboard, with a score of 69.22/100. Its evidence status is Supported, and this profile shows 48 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.
Is GPT-5.5 good for knowledge and understanding?
GPT-5.5 ranks #15 out of 187 eligible models for knowledge and understanding, with a public category score of 72.1/100. 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.
Is GPT-5.5 good for coding and programming?
GPT-5.5 ranks #14 out of 152 eligible models for coding and programming, with a public category score of 62/100. 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-5.5 good for mathematics?
GPT-5.5 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-5.5 good for reasoning and logic?
GPT-5.5 ranks #17 out of 27 eligible models for reasoning and logic, with a public category score of 70.3/100. 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-5.5 good for agentic tool use and computer tasks?
GPT-5.5 ranks #23 out of 131 eligible models for agentic tool use and computer tasks, with a public category score of 62.7/100. 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-5.5 good for multimodal and grounded tasks?
GPT-5.5 ranks #23 out of 55 eligible models for multimodal and grounded tasks, with a public category score of 75.5/100. 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-5.5?
GPT-5.5 belongs to the GPT-5.5 family. Related tracked variants include GPT-5.5 Pro. 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-5.5 have full benchmark coverage on BenchLM?
No. GPT-5.5 currently has 66 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-5.5?
GPT-5.5 has a documented context window of 1M. 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.