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Model comparison

GPT-5.5 Pro vs Qwen2.5 Coder 32B Instruct

Data verified

Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

No sourced benchmark result is currently shared by both models. This page therefore compares only the available metadata, pricing, and runtime rows; it does not name a quality winner.
63.69/100
Margin
29.0pts
← winning
0 category wins0 category wins

Public leaderboard positions: GPT-5.5 Pro #38 (Estimated); Qwen2.5 Coder 32B Instruct #186 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. GPT-5.5 Pro and Qwen2.5 Coder 32B Instruct share 0 comparable benchmark results. 0 of 8 categories are comparable. 7 results are unique to GPT-5.5 Pro; 4 to Qwen2.5 Coder 32B Instruct.

Updated July 22, 2026
Shared results
0
GPT-5.5 Pro only
7
Qwen2.5 Coder 32B Instruct only
4
Comparable categories
0 / 8

Benchmark data for GPT-5.5 Pro and Qwen2.5 Coder 32B Instruct is coming soon on BenchLM.

Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 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.

GPT-5.5 Pro is priced at $30.00 input / $180.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen2.5 Coder 32B Instruct. GPT-5.5 Pro has the larger context window at 1M, compared with 128K for Qwen2.5 Coder 32B Instruct.

Operational comparison

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

MetricGPT-5.5 ProQwen2.5 Coder 32B InstructComparison
Input / output priceUSD per 1M tokensGPT-5.5 Pro$30 input / $180 outputQwen2.5 Coder 32B Instruct$0 input / $0 outputQwen2.5 Coder 32B Instruct has the lower combined listed price.
Generation speedtokens per secondGPT-5.5 ProNot availableQwen2.5 Coder 32B InstructNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenGPT-5.5 ProNot availableQwen2.5 Coder 32B InstructNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensGPT-5.5 Pro1MQwen2.5 Coder 32B Instruct128KGPT-5.5 Pro lists the larger context window.

Benchmark Deep Dive

Agentic
BenchmarkGPT-5.5 ProQwen2.5 Coder 32B InstructResult
BrowseCompSource 90.1%Not comparable
Coding
BenchmarkGPT-5.5 ProQwen2.5 Coder 32B InstructResult
AA-SciCodeSource 27.1%Not comparable
Reasoning
BenchmarkGPT-5.5 ProQwen2.5 Coder 32B InstructResult
CritPtSource 30.6%Not comparable
Knowledge
BenchmarkGPT-5.5 ProQwen2.5 Coder 32B InstructResult
HLESource 57.2%Not comparable
HLE w/o toolsSource 43.1%Not comparable
Artificial Analysis Intelligence IndexSource 7.1%Not comparable
AA-GPQA DiamondSource 41.7%Not comparable
AA-HLESource 3.8%Not comparable
Math
BenchmarkGPT-5.5 ProQwen2.5 Coder 32B InstructResult
FrontierMath (legacy)Source 52.4%Not comparable
FrontierMath v2 (Tiers 1-3)Source 51.000%Not comparable
FrontierMath v2 (Tier 4)Source 39.600%Not comparable
Frequently Asked Questions (3)

Can I compare GPT-5.5 Pro and Qwen2.5 Coder 32B Instruct 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-5.5 Pro and Qwen2.5 Coder 32B Instruct today?

GPT-5.5 Pro: $30.00 input / $180.00 output per 1M tokens Qwen2.5 Coder 32B Instruct: $0.00 input / $0.00 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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Last updated: July 22, 2026

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