Skip to main content
BenchLM
Data

Data as of October 20, 2026 · How the score is built

Superseded.OpenAI has newer models in this line:GPT-5.6 Sol
SupersededProprietaryReasoning

Released Apr 23, 20261M context

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 scores, ranks, weighting, benchmark coverage, and evidence status
CategoryScore
AgenticRank #23 of 131Percentile 83rdWeight 22%15 benchmarksVerified
62.7
CodingRank #14 of 152Percentile 91stWeight 20%10 benchmarksVerified
62.0
ReasoningRank #17 of 27Percentile 38thWeight 17%5 benchmarksVerified
70.3
MultimodalRank #23 of 55Percentile 59thWeight 12%5 benchmarksVerified
75.5
KnowledgeRank #15 of 187Percentile 92ndWeight 12%6 benchmarksVerified
72.1
MultilingualWeight 7%0 benchmarksNot measured
Not measured
Inst. FollowingWeight 5%0 benchmarksNot measured
Not measured
MathRank Not rankedWeight 5%3 benchmarksVerified
91.2

48 of 726 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. Agentic15/15 verified
  2. Coding10/10 verified
  3. Reasoning5/5 verified
  4. Multimodal5/5 verified
  5. Knowledge6/6 verified
  6. MultilingualNot measured
  7. Inst. FollowingNot measured
  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-5.5 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

  1. Agentic#23/131
  2. Coding#14/152
  3. Reasoning#17/27
  4. Multimodal#23/55
  5. Knowledge#15/187
  6. MultilingualNot ranked
  7. Inst. FollowingNot ranked
  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.

Coding10 rows
Coding benchmark values, best verified comparison, weight, and source status
SWE-bench ProScore58.6%Versus best verified row

Best verified: Claude Opus 5.5 · 89.9%

Gap31.3 behindWeight26% ref. weight
CursorBench 3.2Score58.4%Versus best verified row

Best verified: Claude Fable 5.1 · 73.4%

Gap15 behindWeight10% ref. weight
LiveCodeBench (Vals)LiveCodeBench, Vals AI runScore85.3%Versus best verified row

Best verified: Claude Fable 5.1 · 90.5%

Gap5.2 behindWeight8% ref. weight
FrontierCode 1.1 MainScore43.0%Versus best verified row

Best verified: Claude Opus 5.5 · 54.4%

Gap11.4 behindWeight4% ref. weight
Terminal-Bench 2.0Score82.0%Versus best verified row

Best verified: GPT-5.5 · 82.0%

GapBest verifiedWeightScored in Agentic
Vibe Code BenchVibe Code Bench v1.1Score69.85%Versus best verified row

Best verified: Gemini 4 Argon · 91.90%

Gap22.1 behindWeightDisplay only
React Native EvalsScore84.7%Versus best verified row

Best verified: Composer 2 · 96.1%

Gap11.4 behindWeightDisplay only
cursorBench31Score59.2%Versus best verified row

Best verified: Claude Fable 5 · 70.6%

Gap11.4 behindWeightDisplay only
SWE-bench (Vals)SWE-bench, Vals AI runScore82.6%Versus best verified row

Best verified: Claude Opus 5 · 97.0%

Gap14.4 behindWeightDisplay only
PostTrainBench v1.1Score27.2%Versus best verified row

Best verified: Claude Opus 5.5 · 49.3%

Gap22.1 behindWeightDisplay only
Agentic15 rows
Agentic benchmark values, best verified comparison, weight, and source status
BrowseCompScore84.4%Versus best verified row

Best verified: Atria Dawn Preview · 92.5%

Gap8.1 behindWeight8% ref. weight
Terminal-Bench 2.0Score82%Versus best verified row

Best verified: GPT-5.5 · 82%

GapBest verifiedWeight7% ref. weight
OSWorld-VerifiedScore78.7%Versus best verified row

Best verified: Qwen3.8 Max · 86.1%

Gap7.4 behindWeight7% ref. weight
JobBenchScore42.7%Versus best verified row

Best verified: Muse Wonder Full · 75.3%

Gap32.6 behindWeight6% ref. weight
Benchmark exact
MCP AtlasScore75.3%Versus best verified row

Best verified: Muse Spark Open · 89.2%

Gap13.9 behindWeight4% ref. weight
Terminal-Bench 2.1 (Vals)Terminal-Bench 2.1, Vals AI runScore76.4%Versus best verified row

Best verified: GPT-6 Astra · 87.3%

Gap10.9 behindWeight4% ref. weight
ToolathlonScore55.6%Versus best verified row

Best verified: Muse Spark 1.1 · 75.6%

Gap20 behindWeight3% ref. weight
CyberGymScore81.8%Versus best verified row

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

Gap13.3 behindWeightDisplay only
τ²-bench resultsτ²-Bench Tool-Agent-User EvaluationScore98%Versus best verified row

Best verified: GPT-5.4 · 98.9%

Gap0.9 behindWeightDisplay only
Gert LabsGert Labs Composite Game BenchmarkScore72.93%Versus best verified row

Best verified: Claude Opus 4.8 · 72.97%

Gap0 behindWeightDisplay only
Benchmark exact
ResearchClawBenchScore17.0%Versus best verified row

Best verified: Claude Opus 4.8 · 21.1%

Gap4.1 behindWeightDisplay only
OSWorld 2.0Score13.0%Versus best verified row

Best verified: GPT-6 Astra · 72.6%

Gap59.6 behindWeightDisplay only
Benchmark exact
ExploitGymScore13.4%Versus best verified row

Best verified: GPT-6 Astra · 42.4%

Gap29 behindWeightDisplay only
Benchmark exact
ApprenticeBenchApprenticeBench: end-to-end computer use, continual learning, and long-horizon agency on a real accounts-payable jobScore20%Versus best verified row

Best verified: Claude Fable 5.1 · 72%

Gap52 behindWeightDisplay only
MazeBenchMazeBench: visual spatial reasoning in a 3D open worldScore0%Versus best verified row

Best verified: GPT-6 Astra · 23%

Gap23 behindWeightDisplay only
Reasoning5 rows
Reasoning benchmark values, best verified comparison, weight, and source status
ARC-AGI-2Abstraction and Reasoning Corpus for AGI v2Score85%Versus best verified row

Best verified: GPT-6 Astra · 95%

Gap10 behindWeightWeighted 25%
ARC-AGI-3Abstraction and Reasoning Corpus for AGI v3Score0.4%Versus best verified row

Best verified: GPT-6 Astra · 62.7%

Gap62.3 behindWeightWeighted 15%
MRCR v2 64K-128KOpenAI MRCR v2 8-needle 64K-128KScore83.1%Versus best verified row

Best verified: Gemini 3.7 Flash · 97%

Gap13.9 behindWeightDisplay only
MRCR v2 128K-256KOpenAI MRCR v2 8-needle 128K-256KScore87.5%Versus best verified row

Best verified: GPT-5.5 · 87.5%

GapBest verifiedWeightDisplay only
ARC-AGI-1ARC-AGI-1 Semi-Private EvaluationScore95.00%Versus best verified row

Best verified: Claude Fable 5 · 98.50%

Gap3.5 behindWeightDisplay only
Multimodal5 rows
Multimodal benchmark values, best verified comparison, weight, and source status
MMMU-ProMassive Multi-discipline Multimodal Understanding ProScore81.2%Versus best verified row

Best verified: Gemini 3.5 Flash · 83.6%

Gap2.4 behindWeightWeighted 40%
OfficeQA ProScore54.1%Versus best verified row

Best verified: Claude Opus 5.5 · 67.7%

Gap13.6 behindWeightWeighted 25%
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 behindWeightDisplay 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 behindWeightDisplay only
MMMU-Pro w/ PythonMMMU-Pro with PythonScore83.2%Versus best verified row

Best verified: GPT-5.6 Sol · 84.6%

Gap1.4 behindWeightDisplay only
Knowledge6 rows
Knowledge benchmark values, best verified comparison, weight, and source status
HLEHumanity's Last ExamScore52.2%Versus best verified row

Best verified: Claude Fable 5.1 · 65%

Gap12.8 behindWeight44% ref. weight
HLE w/o toolsHumanity's Last Exam without toolsScore41.4%Versus best verified row

Best verified: Claude Opus 5.5 · 64.4%

Gap23 behindWeight7% ref. weight
MMLU-Pro (Vals)MMLU-Pro, Vals AI runScore88.1%Versus best verified row

Best verified: Claude Fable 5.1 · 92.4%

Gap4.3 behindWeight6% ref. weight
GPQAGraduate-Level Google-Proof Q&AScore93.6%Versus best verified row

Best verified: GPT-6 Astra · 96%

Gap2.4 behindWeight3% ref. weight
GPQA Diamond (Vals)GPQA Diamond, Vals AI runScore93.2%Versus best verified row

Best verified: Gemini 3.1 Pro · 95.5%

Gap2.3 behindWeight2% ref. weight
GPQA-DGPQA DiamondScore93.6%Versus best verified row

Best verified: GPT-6 Astra · 96.0%

Gap2.4 behindWeightDisplay only
Math3 rows
Math benchmark values, best verified comparison, weight, and source status
FrontierMath v2 (Tiers 1-3)FrontierMath v2 Tiers 1-3Score85.263%Versus best verified row

Best verified: GPT-6 Astra · 93.684%

Gap8.4 behindWeightWeighted 30%
FrontierMath v2 (Tier 4)FrontierMath v2 Tier 4Score72.500%Versus best verified row

Best verified: GPT-6.1 Sol · 100.000%

Gap27.5 behindWeightWeighted 10%
FrontierMath (legacy)FrontierMath legacy aggregateScore51.7%Versus best verified row

Best verified: GPT-5.6 Sol · 89%

Gap37.3 behindWeightDisplay only
External signals4 rows
External signals benchmark values, best verified comparison, weight, and source status
SEC-Bench ProScore45.8%Versus best verified row

Best verified: GPT-6 Astra · 85.4%

Gap39.6 behindWeightDisplay only
Provider exact
Atomic network attacksAtomic Network Attack SimulationScore100%Versus best verified row

Best verified: GPT-6 Astra · 100%

GapBest verifiedWeightDisplay only
Atomic vulnerability researchAtomic Vulnerability Research and ExploitationScore92%Versus best verified row

Best verified: GPT-6 Astra · 100%

Gap8 behindWeightDisplay only
Atomic evasionAtomic EvasionScore54%Versus best verified row

Best verified: GPT-5.6 Sol · 56%

Gap2 behindWeightDisplay only

Bars run 0–100; the dark tick marks the best source-verified value

All 48 rows

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-5.5 · 69.2 score · $17.50 blended per million tokens

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

405060708090100$0.10$0.50$1$5$10$25$50$100↘ frontierGPT-5.5

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.

  1. Mar 5, 2026

    GPT-5.4

    Score 66.7 · $2.5 / $15

  2. Apr 23, 2026 · you are here

    GPT-5.5

    Score 69.2 · $5 / $30

  3. Jul 9, 2026

    GPT-5.6 Sol

    Score 75.3 · $4 / $20

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-5.5OpenAI 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.

Inference hosts for GPT-5.5 with input, output and cached-input prices per million tokens.
HostInput /1MOutput /1MCached /1MContext
OpenRouter$5.00$30.00$0.501.05M
Compare inference hosts

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.

Watch GPT-5.5 in the weekly brief

Get one weekly email when material rank, price, availability, or benchmark evidence changes are worth revisiting.

Read a sample issue

Join 5,600+ readers.

Compare GPT-5.5 with every tracked model971 comparisons