Noometry

Model comparison

GPT-6.1 Sol vs Qwen2.5 72B Instruct

Qwen2.5 72B Instruct has enough public results to be ranked (#259); GPT-6.1 Sol does not yet, so treat this comparison as directional.

Last verified . 0 shared benchmarks.

GPT-6.1 Sol OpenAI

42.1

Unranked Sparse

Qwen2.5 72B Instruct Alibaba (Qwen)

31.5

Rank #259 Confirmed

Summary

  • The widest gap is in coding, where GPT-6.1 Sol leads 51.4 to 32.8.
  • Qwen2.5 72B Instruct is cheaper at $1.40 / $5.60 per million input/output tokens, against $2 / $10 for GPT-6.1 Sol.
  • GPT-6.1 Sol accepts more context: 1.05M tokens versus 131K.
  • Qwen2.5 72B Instruct has downloadable open weights; the other is API-only.

Side by side

GPT-6.1 SolQwen2.5 72B Instruct
ProviderOpenAIAlibaba (Qwen)
Noometry Index42.131.5
Released2026-09-292024-09
WeightsProprietaryOpen
Context window1.05M131K
Max output128K8K
Input $ / M tokens$2$1.40
Output $ / M tokens$10$5.60
Results tracked243

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Category by category

Coding GPT-6.1 Sol leads

GPT-6.1 Sol: 51.4, Qwen2.5 72B Instruct: 32.8 (#256)

BenchmarkGPT-6.1 SolQwen2.5 72B Instruct
DeepSWE75.2%—
WeirdML—16%
BigCodeBench Instruct—45.8%
LMArena Coding—1293
BigCodeBench Complete—55.9%

Agentic & Tool Use GPT-6.1 Sol leads

GPT-6.1 Sol: 34.3, Qwen2.5 72B Instruct: 22.0 (#124)

BenchmarkGPT-6.1 SolQwen2.5 72B Instruct
TheAgentCompany—5.7%
BALROG—16.2%
GDP.pdf32%—
METR Time Horizons—35.8%

Reasoning Not comparable

GPT-6.1 Sol: —, Qwen2.5 72B Instruct: 21.4 (#197)

BenchmarkGPT-6.1 SolQwen2.5 72B Instruct
LMArena Hard Prompts—1271
DTBench—62.9%
LMCA—13.4%
BIG-Bench Hard—79.8%
Epoch Capabilities Index—129.01
ForecastBench—57.5
HellaSwag—84.8%
PIQA—82.6%
WinoGrande—82.3%

Math Not comparable

GPT-6.1 Sol: —, Qwen2.5 72B Instruct: 18.1 (#281)

BenchmarkGPT-6.1 SolQwen2.5 72B Instruct
OTIS Mock AIME 2024-2025—8.1%
Omni-MATH—33%
LMArena Math—1283
MATH Level 5—63.2%

Knowledge Not comparable

GPT-6.1 Sol: —, Qwen2.5 72B Instruct: 26.9 (#247)

BenchmarkGPT-6.1 SolQwen2.5 72B Instruct
GPQA Diamond—49.1%
MMLU-Pro—63.1%
Confabulations—19.1%
GPQA (HELM)—42.6%
LMArena Expert—1245
ARC (AI2) Challenge—94.5%
MMLU—85.3%
TriviaQA—71.9%

Multilingual Not comparable

GPT-6.1 Sol: —, Qwen2.5 72B Instruct: 41.2 (#208)

BenchmarkGPT-6.1 SolQwen2.5 72B Instruct
LMArena Non-English—1252
LMArena Chinese—1272
LMArena French—1279
LMArena German—1234
LMArena Japanese—1180
LMArena Korean—1188
LMArena Russian—1264
LMArena Spanish—1255

Instruction Following Not comparable

GPT-6.1 Sol: —, Qwen2.5 72B Instruct: 65.7 (#216)

BenchmarkGPT-6.1 SolQwen2.5 72B Instruct
IFEval—80.6%
LMArena Instruction Following—1254

Long Context Not comparable

GPT-6.1 Sol: —, Qwen2.5 72B Instruct: 38.9 (#183)

BenchmarkGPT-6.1 SolQwen2.5 72B Instruct
LMArena Longer Query—1282

Writing & Preference Not comparable

GPT-6.1 Sol: —, Qwen2.5 72B Instruct: 46.9 (#210)

BenchmarkGPT-6.1 SolQwen2.5 72B Instruct
LMArena Text—1269
LMArena Creative Writing—1221
WildBench—80.2%
LMArena Multi-Turn—1272

Frequently asked questions

Is GPT-6.1 Sol better than Qwen2.5 72B Instruct?

Qwen2.5 72B Instruct has enough public results to be ranked (#259); GPT-6.1 Sol does not yet, so treat this comparison as directional.

Which is cheaper, GPT-6.1 Sol or Qwen2.5 72B Instruct?

Qwen2.5 72B Instruct is cheaper. It lists at $1.40 per million input tokens and $5.60 per million output tokens; GPT-6.1 Sol lists at $2 and $10.

Is GPT-6.1 Sol or Qwen2.5 72B Instruct better for coding?

GPT-6.1 Sol scores higher on coding benchmarks: 51.4 versus 32.8 in the Noometry coding category.

Which has the bigger context window?

GPT-6.1 Sol does, with 1.05M tokens against 131K.

How many benchmarks do GPT-6.1 Sol and Qwen2.5 72B Instruct share?

0 benchmarks have published results for both models. GPT-6.1 Sol has 2 scored results on Noometry and Qwen2.5 72B Instruct has 43.

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