GPT-5.2-Codex vs Qwen3.7 Max

Updated October 2, 2026. Rank says Qwen3.7 Max is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Decision reading

Qwen3.7 Max has the higher public point estimate, 63.46 versus 56.76. Their conditional score ranges overlap. These ranges do not establish rank confidence. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A

GPT-5.2-Codex

OpenAI

56.76/100

Estimated · Public rank #58

Conditional range 47.0–66.5

Model B

Qwen3.7 Max

Alibaba

63.46/100

Supported · Public rank #43

90% interval 54.0–72.9

Shared results: 3
GPT-5.2-Codex only: 2
Qwen3.7 Max only: 38
Like-for-like categories: 1 / 8

Estimated: GPT-5.2-Codex · Supported: Qwen3.7 Max. Conditional ranges do not establish rank confidence.How the comparison works

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

Coding work
Code generation, repair, and software-engineering tasks
GPT-5.2-Codex
GPT-5.2-Codex has the higher public coding point estimate, 46.5 to 45.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Qwen3.7 Max
Qwen3.7 Max has the larger documented context window.
Confidence: documented

Show secondary and unsupported calls

Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-5.2-Codex is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

Pick the job you would use them for

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

46.5GPT-5.2-Codex45.4Qwen3.7 Max

Like-for-like · BenchAlign v5.8

GPT-5.2-Codex has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboard Shortlist models for this job

Same basis rules as the category table below

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

LiveCodeBench (Vals)Coding
Normalized gap 0.9
GPT-5.2-Codex:88.0%
Qwen3.7 Max:87.1%
GPT-5.2-Codex source Qwen3.7 Max source

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Coding

Like-for-like

GPT-5.2-Codex: 46.5; Supported · #49/144
Qwen3.7 Max: 45.4; Supported · #54/144
Basis: BenchAlign v5.8 lane · 3 vs 10 public rows
Reading: GPT-5.2-Codex leads · intervals overlap

Agentic

Directional only

GPT-5.2-Codex: 40.3; Estimated · #57/119
Qwen3.7 Max: 39.3; Supported · #60/119
Basis: BenchAlign v5.8 lane · 2 vs 10 public rows
Reading: Directional only

Knowledge

Directional only

GPT-5.2-Codex: 55.5; Estimated · #49/171
Qwen3.7 Max: 59.8; Supported · #41/171
Basis: BenchAlign v5.8 lane · 0 vs 9 public rows
Reading: Directional only

Instruction following

Directional only

GPT-5.2-Codex: 92.4; #3/125
Qwen3.7 Max: 89.2; #17/125
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Directional only

Reasoning

Not comparable

GPT-5.2-Codex: 78.9; Unranked · 2 rankable rows
Qwen3.7 Max: 76.3; Unranked · 3 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Multimodal

Not comparable

GPT-5.2-Codex: 73.5; Unranked · 1 rankable row
Qwen3.7 Max: Not ranked
Basis: Provisional lane · 0 vs 0 weighted rows
Reading: Not comparable

Multilingual

Not comparable

GPT-5.2-Codex: Not ranked
Qwen3.7 Max: 100.0; #1/16
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Math

Not comparable

GPT-5.2-Codex: Not ranked
Qwen3.7 Max: 81.9; Unranked · 3 rankable rows
Basis: Provisional lane · 0 vs 1 weighted rows
Reading: Not comparable

Category scores with the server-provided evidence basis for GPT-5.2-Codex and Qwen3.7 Max
Category	GPT-5.2-Codex	Qwen3.7 Max	Basis	Reading
Coding	46.5Supported · #49/144	45.4Supported · #54/144	Like-for-likeBenchAlign v5.8 lane · 3 vs 10 public rows	GPT-5.2-Codex leads · intervals overlap
Agentic	40.3Estimated · #57/119	39.3Supported · #60/119	Directional onlyBenchAlign v5.8 lane · 2 vs 10 public rows	Directional only
Knowledge	55.5Estimated · #49/171	59.8Supported · #41/171	Directional onlyBenchAlign v5.8 lane · 0 vs 9 public rows	Directional only
Instruction following	92.4#3/125	89.2#17/125	Directional onlyProvisional lane · 0 vs 1 weighted rows	Directional only
Reasoning	78.9Unranked · 2 rankable rows	76.3Unranked · 3 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Multimodal	73.5Unranked · 1 rankable row	Not ranked	Not comparableProvisional lane · 0 vs 0 weighted rows	Not comparable
Multilingual	Not ranked	100.0#1/16	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable
Math	Not ranked	81.9Unranked · 3 rankable rows	Not comparableProvisional lane · 0 vs 1 weighted rows	Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

GPT-5.2-Codex: $0.00875; Fits in one request
Qwen3.7 Max: API rate not published; Fits in one request

Qwen3.7 Max has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GPT-5.2-Codex: $0.1295; Fits in one request
Qwen3.7 Max: API rate not published; Fits in one request

Qwen3.7 Max has no comparable published API token rate.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

GPT-5.2-Codex: $0.525; Fits in one request; Cached input priced at the published list-input rate
Qwen3.7 Max: API rate not published; Fits in one request; Cached-input rate unavailable

GPT-5.2-Codex has no published cached-input rate, so cached tokens use its listed input rate. Qwen3.7 Max has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationGPT-5.2-CodexQwen3.7 Max

Context window

Maximum documented context; output-token limits may be lower.

GPT-5.2-Codex

400K

Qwen3.7 Max

API model ID

GPT-5.2-Codex

Not sourced

Qwen3.7 Max

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

GPT-5.2-Codex

Not published

Qwen3.7 Max

No comparable hosted API rate

Documented inputs

GPT-5.2-Codex

Not sourced

Qwen3.7 Max

Not sourced

Documented outputs

GPT-5.2-Codex

Not sourced

Qwen3.7 Max

Not sourced

Provider availability

GPT-5.2-Codex

Not sourced

Qwen3.7 Max

Not sourced

Reasoning profile

GPT-5.2-Codex

Reasoning

Qwen3.7 Max

Reasoning

Weight access

GPT-5.2-Codex

Proprietary

Qwen3.7 Max

Proprietary

License

GPT-5.2-Codex

Proprietary

Qwen3.7 Max

Proprietary

Release date

GPT-5.2-Codex

2025-12-18

Qwen3.7 Max

2026-05-16

If you already use one of these models

Deployment change: The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal: Qwen3.7 Max has the higher public point estimate, 63.46 versus 56.76. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost: A complete comparable API-rate estimate is not available for both models.
Context tradeoff: Qwen3.7 Max has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, GPT-5.2-Codex or Qwen3.7 Max?

Qwen3.7 Max has the higher public point estimate, 63.46 versus 56.76. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, GPT-5.2-Codex or Qwen3.7 Max?

GPT-5.2-Codex has the higher public coding point estimate, 46.5 to 45.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.

Which is better for agentic tasks, GPT-5.2-Codex or Qwen3.7 Max?

GPT-5.2-Codex scores higher for agentic tasks on the public lane, 40.3 to 39.3. GPT-5.2-Codex is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, GPT-5.2-Codex or Qwen3.7 Max?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, GPT-5.2-Codex or Qwen3.7 Max?

Qwen3.7 Max has the larger documented context window: 1M, compared with 400K.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence43 rows

Agentic

Gert Labs
Shared source
GPT-5.2-Codex51.79%
Qwen3.7 Max64.27%
Qwen3.7 Max leads this result
JobBench
GPT-5.2-Codex26.0%
Source
Qwen3.7 Max—
Not directly comparable
Terminal-Bench 2.0
GPT-5.2-Codex—
Qwen3.7 Max69.7%
Source
Not directly comparable
QwenClawBench
GPT-5.2-Codex—
Qwen3.7 Max64.3%
Source
Not directly comparable
Claw-Eval
GPT-5.2-Codex—
Qwen3.7 Max65.2%
Source
Not directly comparable
BFCL v4
GPT-5.2-Codex—
Qwen3.7 Max75.0%
Source
Not directly comparable
MCP Atlas
GPT-5.2-Codex—
Qwen3.7 Max76.4%
Source
Not directly comparable
VITA-Bench
GPT-5.2-Codex—
Qwen3.7 Max47.9%
Source
Not directly comparable
HLE w/ tools
GPT-5.2-Codex—
Qwen3.7 Max53.5%
Source
Not directly comparable
ResearchClawBench
GPT-5.2-Codex—
Qwen3.7 Max18.7%
Source
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.2-Codex—
Qwen3.7 Max61.0%
Source
Not directly comparable

Coding

Vibe Code Bench
GPT-5.2-Codex37.91%
Source
Qwen3.7 Max—
Not directly comparable
LiveCodeBench (Vals)
GPT-5.2-Codex88.0%
Source
Qwen3.7 Max87.1%
Source
GPT-5.2-Codex leads this result
SWE-bench (Vals)
GPT-5.2-Codex72.4%
Source
Qwen3.7 Max68.8%
Source
GPT-5.2-Codex leads this result
SWE-bench Verified
GPT-5.2-Codex—
Qwen3.7 Max80.4%
Source
Not directly comparable
SWE-bench Pro
GPT-5.2-Codex—
Qwen3.7 Max60.6%
Source
Not directly comparable
SWE Multilingual
GPT-5.2-Codex—
Qwen3.7 Max78.3%
Source
Not directly comparable
NL2Repo
GPT-5.2-Codex—
Qwen3.7 Max47.2%
Source
Not directly comparable
SciCode
GPT-5.2-Codex—
Qwen3.7 Max53.5%
Source
Not directly comparable
LiveCodeBench
GPT-5.2-Codex—
Qwen3.7 Max91.6%
Source
Not directly comparable
Terminal-Bench 2.0
GPT-5.2-Codex—
Qwen3.7 Max69.7%
Source
Not directly comparable
OpenHarmony Bench
GPT-5.2-Codex—
Qwen3.7 Max53.4%
Source
Not directly comparable

Reasoning

MRCRv2
GPT-5.2-Codex—
Qwen3.7 Max90.4%
Source
Not directly comparable
CritPt
GPT-5.2-Codex—
Qwen3.7 Max13.4%
Source
Not directly comparable

Knowledge

GPQA
GPT-5.2-Codex—
Qwen3.7 Max92.4%
Source
Not directly comparable
GPQA-D
GPT-5.2-Codex—
Qwen3.7 Max92.4%
Source
Not directly comparable
HLE
GPT-5.2-Codex—
Qwen3.7 Max41.4%
Source
Not directly comparable
MMLU-Pro
GPT-5.2-Codex—
Qwen3.7 Max89.6%
Source
Not directly comparable
MMLU-Redux
GPT-5.2-Codex—
Qwen3.7 Max95%
Source
Not directly comparable
SuperGPQA
GPT-5.2-Codex—
Qwen3.7 Max73.6%
Source
Not directly comparable
MMMLU
GPT-5.2-Codex—
Qwen3.7 Max90.3%
Source
Not directly comparable
GPQA Diamond (Vals)
GPT-5.2-Codex—
Qwen3.7 Max90.2%
Source
Not directly comparable
MMLU-Pro (Vals)
GPT-5.2-Codex—
Qwen3.7 Max89.3%
Source
Not directly comparable

Multilingual

MMLU-ProX
GPT-5.2-Codex—
Qwen3.7 Max87%
Source
Not directly comparable
NOVA-63
GPT-5.2-Codex—
Qwen3.7 Max59.0%
Source
Not directly comparable
INCLUDE
GPT-5.2-Codex—
Qwen3.7 Max86.2%
Source
Not directly comparable
MAXIFE
GPT-5.2-Codex—
Qwen3.7 Max89.2%
Source
Not directly comparable
PolyMath
GPT-5.2-Codex—
Qwen3.7 Max86.5%
Source
Not directly comparable

Instruction following

IFEval
GPT-5.2-Codex—
Qwen3.7 Max94.3%
Source
Not directly comparable
IFBench
GPT-5.2-Codex—
Qwen3.7 Max79.1%
Source
Not directly comparable

Math

HMMT Feb 2026
GPT-5.2-Codex—
Qwen3.7 Max97.1%
Source
Not directly comparable
IMOAnswerBench
GPT-5.2-Codex—
Qwen3.7 Max90.0%
Source
Not directly comparable
Apex
GPT-5.2-Codex—
Qwen3.7 Max44.5%
Source
Not directly comparable

43 public results · 3 shared

Related comparisons

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Last updated October 2, 2026

GPT-5.2-Codex vs Qwen3.7 Max

Decision reading

Which one for your work

Coding work

Long documents

Agentic work

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Which one for a specific job

What is actually comparable

Shape of the matched evidence

Category results, on a stated basis

Coding

Agentic

Knowledge

Instruction following

Reasoning

Multimodal

Multilingual

Math

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

If you already use one of these models

Questions

Which is better, GPT-5.2-Codex or Qwen3.7 Max?

Which is better for coding, GPT-5.2-Codex or Qwen3.7 Max?

Which is better for agentic tasks, GPT-5.2-Codex or Qwen3.7 Max?

Which costs less, GPT-5.2-Codex or Qwen3.7 Max?

Which has the larger context window, GPT-5.2-Codex or Qwen3.7 Max?

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Multilingual

Instruction following

Math

Related comparisons

Watch GPT-5.2-Codex vs Qwen3.7 Max