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Model comparison

GPT-5.2-Codex vs Qwen3.7 Plus

Data verified

Head-to-head evidence from 12 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

59.1/100
Margin
8.1pts
winning →
67.22/100
0 category wins0 category wins

Public leaderboard positions: GPT-5.2-Codex #58 (Supported); Qwen3.7 Plus #21 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GPT-5.2-Codex and Qwen3.7 Plus share 12 comparable benchmark results. 0 of 8 categories are comparable. 3 results are unique to GPT-5.2-Codex; 57 to Qwen3.7 Plus.

Updated July 23, 2026
Shared results
12
GPT-5.2-Codex only
3
Qwen3.7 Plus only
57
Comparable categories
0 / 8

Benchmark data for GPT-5.2-Codex and Qwen3.7 Plus is coming soon on BenchLM.

Confidence note. This is a partial-evidence comparison with 12 shared benchmark results across 6 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.

Qwen3.7 Plus has the larger context window at 1M, compared with 400K for GPT-5.2-Codex.

Category breakdown

Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.

Category scores and score margins for GPT-5.2-Codex and Qwen3.7 Plus
CategoryGPT-5.2-CodexΔQwen3.7 Plus
AgenticGPT-5.2-CodexNot measuredMarginNo overlapQwen3.7 Plus71.7
CodingGPT-5.2-CodexNot measuredMarginNo overlapQwen3.7 Plus75.6
ReasoningGPT-5.2-CodexNot measuredMarginNo overlapQwen3.7 Plus91.7
KnowledgeGPT-5.2-CodexNot measuredMarginNo overlapQwen3.7 Plus60.1
MathGPT-5.2-CodexNot measuredMarginNo overlapQwen3.7 Plus92.9
MultilingualGPT-5.2-CodexNot measuredMarginNo overlapQwen3.7 Plus85.4
MultimodalGPT-5.2-CodexNot measuredMarginNo overlapQwen3.7 Plus81.5
Inst. FollowingGPT-5.2-CodexNot measuredMarginNo overlapQwen3.7 Plus84.5

Operational comparison

Runtime and commercial metrics are compared only when both models have a complete sourced value.

MetricGPT-5.2-CodexQwen3.7 PlusComparison
Input / output priceUSD per 1M tokensGPT-5.2-Codex$1.75 input / $14 outputQwen3.7 PlusNot availableA complete price comparison is not available.
Generation speedtokens per secondGPT-5.2-Codex123 tok/sQwen3.7 PlusNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenGPT-5.2-Codex87.34 sQwen3.7 PlusNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensGPT-5.2-Codex400KQwen3.7 Plus1MQwen3.7 Plus lists the larger context window.

Benchmark Deep Dive

Agentic
BenchmarkGPT-5.2-CodexQwen3.7 PlusResult
τ²-bench resultsSource 92.1%93%Qwen3.7 Plus leads
Gert LabsSource 51.79%—Not comparable
JobBenchSource 26.0%—Not comparable
Terminal-Bench 2.0Source —70.3%Not comparable
QwenClawBenchSource —61.8%Not comparable
QwenWebBenchSource —1536Not comparable
Claw-EvalSource —62.7%Not comparable
BFCL v4Source —72.9%Not comparable
MCP AtlasSource —73.2%Not comparable
VITA-BenchSource —45.6%Not comparable
DeepPlanningSource —62.3%Not comparable
OSWorld-VerifiedSource —73.3%Not comparable
AndroidWorldSource —81.0%Not comparable
AA Agentic IndexSource —20.8%Not comparable
APEX-Agents-AASource —22.4%Not comparable
GDPval-AASource —21.8%Not comparable
GDPval-AASource —936Not comparable
OSWorld 2.0Source —2.8%Not comparable
Coding
BenchmarkGPT-5.2-CodexQwen3.7 PlusResult
Vibe Code BenchSource 37.91%—Not comparable
AA-SciCodeSource 54.6%45.5%GPT-5.2-Codex leads
Terminal-Bench 2.0Source —70.3%Not comparable
SWE-bench VerifiedSource —77.7%Not comparable
SWE-bench ProSource —57.6%Not comparable
SWE MultilingualSource —75.8%Not comparable
NL2RepoSource —41.1%Not comparable
SciCodeSource —51.3%Not comparable
LiveCodeBenchSource —89.6%Not comparable
AA Coding IndexSource —55.9%Not comparable
Reasoning
BenchmarkGPT-5.2-CodexQwen3.7 PlusResult
AA-LCRSource 75.7%65.0%GPT-5.2-Codex leads
CritPtSource 8.7%9.1%Qwen3.7 Plus leads
MRCRv2Source —91.7%Not comparable
Knowledge
BenchmarkGPT-5.2-CodexQwen3.7 PlusResult
Artificial Analysis Intelligence IndexSource 40.1%39.0%GPT-5.2-Codex leads
AA-GPQA DiamondSource 89.9%90.0%Qwen3.7 Plus leads
AA-HLESource 33.5%33.4%GPT-5.2-Codex leads
AA-Omniscience IndexSource -2.5%2.4%Qwen3.7 Plus leads
AA-Omniscience AccuracySource 40.7%22.2%GPT-5.2-Codex leads
AA-Omniscience Hallucination RateSource 72.8%25.5%Qwen3.7 Plus leads
GPQASource —90.3%Not comparable
GPQA-DSource —90.3%Not comparable
HLESource —34.7%Not comparable
MMLU-ProSource —88.5%Not comparable
MMLU-ReduxSource —94.5%Not comparable
SuperGPQASource —71.4%Not comparable
MMMLUSource —89.0%Not comparable
Math
BenchmarkGPT-5.2-CodexQwen3.7 PlusResult
HMMT Feb 2026Source —92.9%Not comparable
IMOAnswerBenchSource —86.0%Not comparable
ApexSource —22.7%Not comparable
Multilingual
BenchmarkGPT-5.2-CodexQwen3.7 PlusResult
MMLU-ProXSource —85.4%Not comparable
NOVA-63Source —58.8%Not comparable
INCLUDESource —83.0%Not comparable
MAXIFESource —88.8%Not comparable
PolyMathSource —84.0%Not comparable
Multimodal
BenchmarkGPT-5.2-CodexQwen3.7 PlusResult
AA-MMMU-ProSource 76.3%80.5%Qwen3.7 Plus leads
MMMU-ProSource —79%Not comparable
MathVisionSource —90.3%Not comparable
CharXivSource —85.9%Not comparable
ERQASource —69.8%Not comparable
MedXpertQA (MM)Source —71.0%Not comparable
ScreenSpot ProSource —79.0%Not comparable
SimpleVQASource —81.7%Not comparable
MMSearch-PlusSource —41.4%Not comparable
RealWorldQASource —86.9%Not comparable
OmniDocBench 1.5Source —91.4%Not comparable
OCRBench V2Source —70.7%Not comparable
ODINW13Source —51.1%Not comparable
Video-MME (with subtitle)Source —88.0%Not comparable
VideoMMMUSource —85.4%Not comparable
MLVU (M-Avg)Source —87.4%Not comparable
Design Arena WebsiteSource —1288Not comparable
Inst. Following
BenchmarkGPT-5.2-CodexQwen3.7 PlusResult
AA-IFBenchSource 77.6%78.0%Qwen3.7 Plus leads
IFEvalSource —94.6%Not comparable
IFBenchSource —79.1%Not comparable
Frequently Asked Questions (3)

Can I compare GPT-5.2-Codex and Qwen3.7 Plus on BenchLM yet?

Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.

Why does this comparison show “coming soon”?

BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.

What data is available for GPT-5.2-Codex and Qwen3.7 Plus today?

GPT-5.2-Codex: $1.75 input / $14.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.

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