Noometry

Model comparison

GPT-5.3 Chat vs Qwen2.5 7B Instruct

GPT-5.3 Chat is the stronger model overall, scoring 42.1 to 28.6 on the Noometry Index. Qwen2.5 7B Instruct costs 16× less per token, which makes it the better buy when GPT-5.3 Chat's lead doesn't matter for your workload.

Last verified . 0 shared benchmarks.

GPT-5.3 Chat OpenAI

42.1

Rank #109 Confirmed

Qwen2.5 7B Instruct Alibaba (Qwen)

28.6

Rank #314 Confirmed

Summary

  • The widest gap is in math, where GPT-5.3 Chat leads 35.4 to 11.8.
  • Qwen2.5 7B Instruct is cheaper at $0.17 / $0.70 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Chat.
  • Qwen2.5 7B Instruct accepts more context: 131K tokens versus 128K.
  • Qwen2.5 7B Instruct has downloadable open weights; the other is API-only.

Side by side

GPT-5.3 ChatQwen2.5 7B Instruct
ProviderOpenAIAlibaba (Qwen)
Noometry Index42.128.6
Released2026-03-032024-09
WeightsProprietaryOpen
Context window128K131K
Max output16K8K
Input $ / M tokens$1.75$0.17
Output $ / M tokens$14$0.70
Results tracked1815

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Category by category

Coding GPT-5.3 Chat leads

GPT-5.3 Chat: 40.8 (#124), Qwen2.5 7B Instruct: 35.9 (#204)

BenchmarkGPT-5.3 ChatQwen2.5 7B Instruct
BigCodeBench Instruct—37.6%
LMArena Coding1408—
BigCodeBench Complete—46.1%

Agentic & Tool Use Not comparable

GPT-5.3 Chat: —, Qwen2.5 7B Instruct: 23.5 (#117)

BenchmarkGPT-5.3 ChatQwen2.5 7B Instruct
BALROG—7.8%

Reasoning GPT-5.3 Chat leads

GPT-5.3 Chat: 27.3 (#105), Qwen2.5 7B Instruct: 14.3 (#317)

BenchmarkGPT-5.3 ChatQwen2.5 7B Instruct
Chess Puzzles—0%
LMArena Hard Prompts1399—
DTBench—47.7%
LMCA—6.4%
Epoch Capabilities Index—118.44

Math GPT-5.3 Chat leads

GPT-5.3 Chat: 35.4 (#146), Qwen2.5 7B Instruct: 11.8 (#300)

BenchmarkGPT-5.3 ChatQwen2.5 7B Instruct
OTIS Mock AIME 2024-2025—2.5%
Omni-MATH—29.4%
LMArena Math1389—

Knowledge GPT-5.3 Chat leads

GPT-5.3 Chat: 38.2 (#137), Qwen2.5 7B Instruct: 17.0 (#281)

BenchmarkGPT-5.3 ChatQwen2.5 7B Instruct
GPQA Diamond—35.5%
MMLU-Pro—53.9%
GPQA (HELM)—34.1%
LMArena Expert1397—
MMLU—72.9%

Multilingual Not comparable

GPT-5.3 Chat: 50.5 (#119), Qwen2.5 7B Instruct: —

BenchmarkGPT-5.3 ChatQwen2.5 7B Instruct
LMArena Non-English1383—
LMArena Chinese1433—
LMArena French1398—
LMArena German1384—
LMArena Japanese1351—
LMArena Korean1347—
LMArena Russian1400—
LMArena Spanish1371—

Instruction Following GPT-5.3 Chat leads

GPT-5.3 Chat: 73.0 (#124), Qwen2.5 7B Instruct: 63.4 (#226)

BenchmarkGPT-5.3 ChatQwen2.5 7B Instruct
IFEval—74.1%
LMArena Instruction Following1378—

Long Context Not comparable

GPT-5.3 Chat: 42.6 (#115), Qwen2.5 7B Instruct: —

BenchmarkGPT-5.3 ChatQwen2.5 7B Instruct
LMArena Longer Query1396—

Writing & Preference GPT-5.3 Chat leads

GPT-5.3 Chat: 63.3 (#65), Qwen2.5 7B Instruct: 48.8 (#194)

BenchmarkGPT-5.3 ChatQwen2.5 7B Instruct
LMArena Text1389—
LMArena Creative Writing1356—
EQ-Bench Creative Writing1690—
WildBench—73.1%
LMArena Multi-Turn1413—

Frequently asked questions

Is GPT-5.3 Chat better than Qwen2.5 7B Instruct?

GPT-5.3 Chat is the stronger model overall, scoring 42.1 to 28.6 on the Noometry Index. Qwen2.5 7B Instruct costs 16× less per token, which makes it the better buy when GPT-5.3 Chat's lead doesn't matter for your workload.

Which is cheaper, GPT-5.3 Chat or Qwen2.5 7B Instruct?

Qwen2.5 7B Instruct is cheaper. It lists at $0.17 per million input tokens and $0.70 per million output tokens; GPT-5.3 Chat lists at $1.75 and $14.

Is GPT-5.3 Chat or Qwen2.5 7B Instruct better for coding?

GPT-5.3 Chat scores higher on coding benchmarks: 40.8 versus 35.9 in the Noometry coding category.

Which has the bigger context window?

Qwen2.5 7B Instruct does, with 131K tokens against 128K.

How many benchmarks do GPT-5.3 Chat and Qwen2.5 7B Instruct share?

0 benchmarks have published results for both models. GPT-5.3 Chat has 18 scored results on Noometry and Qwen2.5 7B Instruct has 15.

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