Data as of October 17, 2026 · How the score is built
Released Dec 18, 2025400K context
GPT-5.2-Codex
Decision readingGPT-5.2-Codex scores 55.3 out of 100 and ranks #66 of 224. This profile shows 5 source-displayable benchmark rows; its strongest eligible category is Coding at #56. API pricing is $1.75 input and $14 output per million tokens.
Released Dec 18, 2025 — 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
55.3/100
field median 49.6#66 of 224 ranked models
Public
#66of 224
Verified —
Price
$1.75input / $14 output
input median $0.75blended $7.88
Speed
117tok/s
field median 92 tok/sFirst token 93.21 s
Context
400Ktokens
field median 256,000Reported for this model; direct source link not stored
Strongest published evidence
Coding ranks #56. Particularly well-suited for software development and code generation tasks.
Validate before choosing
5 published rows leave some tracked benchmark slots empty. Agentic is its lowest eligible category at #64.
Source-linked · 5 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 #64 of 128Percentile 50thWeight 22%2 benchmarksVerified | 40.8 | 2 benchmarks | Verified | |||
| CodingRank #56 of 150Percentile 63rdWeight 20%3 benchmarksVerified | 43.7 | 3 benchmarks | Verified | |||
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| KnowledgeWeight 12%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| Inst. FollowingWeight 5%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured | |||
| MathWeight 5%0 benchmarksNot measured | Not measured | 0 benchmarks | Not measured |
5 of 681 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.
- Agentic2/2 verified
- Coding3/3 verified
- ReasoningNot measured
- MultimodalNot measured
- KnowledgeNot measured
- MultilingualNot measured
- Inst. FollowingNot measured
- MathNot 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-5.2-Codex category percentile values
- Agentic50th percentile
- Coding63rd percentile
- ReasoningNot eligible
- MultimodalNot eligible
- KnowledgeNot eligible
- MultilingualNot eligible
- Instruction followingNot eligible
- MathNot eligible
The dashed outline is median of 6 nearest peers.
Eligible category ranks
- Agentic#64/128
- Coding#56/150
- ReasoningNot ranked
- MultimodalNot ranked
- KnowledgeNot ranked
- 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.
Coding3 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| LiveCodeBench (Vals)LiveCodeBench, Vals AI run | Score88.0% | Versus best verified row Best verified: Claude Fable 5.1 · 90.5% | Gap2.5 behind | Weight8% ref. weight | |
| Vibe Code BenchVibe Code Bench v1.1 | Score37.91% | Versus best verified row Best verified: Gemini 4 Argon · 91.90% | Gap54 behind | WeightDisplay only | Benchmark exact |
| SWE-bench (Vals)SWE-bench, Vals AI run | Score72.4% | Versus best verified row Best verified: Claude Opus 5 · 97.0% | Gap24.6 behind | WeightDisplay only | Verified |
Agentic2 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| JobBench | Score26.0% | Versus best verified row Best verified: Muse Wonder Full · 75.3% | Gap49.3 behind | Weight6% ref. weight | Benchmark exact |
| Gert LabsGert Labs Composite Game Benchmark | Score51.79% | Versus best verified row Best verified: Claude Opus 4.8 · 72.97% | Gap21.2 behind | WeightDisplay only | Benchmark exact |
Bars run 0–100; the dark tick marks the best source-verified value
All 5 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-5.2-Codex · 55.3 score · $7.88 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
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
- Not published
- Context window
- 400K
- 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
- Established
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Not documented in the pricing record
- 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-5.2-Codex ranks #66 of 224 on the public leaderboard with a score of 55.3/100. It does not yet have enough sourced coverage for a verified position.
GPT-5.2-Codex is a proprietary model with a 400K context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.
5 of 681 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Coding at #56, while its lowest eligible position is Agentic at #64. particularly well-suited for software development and code generation tasks.
Last updated October 17, 2026. Runtime fields remain blank until a sourced snapshot exists.
Questions
How does GPT-5.2-Codex perform overall in AI benchmarks?
GPT-5.2-Codex ranks #66 out of 224 models on the public BenchAlign leaderboard, with a score of 55.3/100. Its evidence status is Estimated, and this profile shows 5 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.
Is GPT-5.2-Codex good for coding and programming?
GPT-5.2-Codex ranks #56 out of 150 eligible models for coding and programming, with a public category score of 43.7/100. 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-5.2-Codex good for agentic tool use and computer tasks?
GPT-5.2-Codex ranks #64 out of 128 eligible models for agentic tool use and computer tasks, with a public category score of 40.8/100. 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.
Does GPT-5.2-Codex have full benchmark coverage on BenchLM?
No. GPT-5.2-Codex currently has 16 source-displayable rows across 681 tracked benchmark slots. 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-5.2-Codex?
GPT-5.2-Codex has a reported context window of 400K in the exact-model catalog record. The value stays visible, but the profile marks its source link as unavailable instead of presenting it as directly documented. Maximum output length remains separate because providers often publish a different limit.