Noometry

Model comparison

GPT-4 Turbo vs Qwen3.5-Flash

Qwen3.5-Flash is the stronger model overall, scoring 42.5 to 30.5 on the Noometry Index.

Last verified . 24 shared benchmarks.

GPT-4 Turbo OpenAI

30.5

Rank #292 Confirmed

Qwen3.5-Flash Alibaba (Qwen)

42.5

Rank #112 Confirmed

Summary

  • They share 24 benchmarks with published results for both. GPT-4 Turbo scores higher in 0 categories and Qwen3.5-Flash in 8 categories; 7 gaps are clear of the uncertainty.
  • The widest gap is in math, where Qwen3.5-Flash leads 37.4 to 9.0.
  • The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 6.7% for GPT-4 Turbo and 84.4% for Qwen3.5-Flash.
  • Qwen3.5-Flash is cheaper at $0.10 / $0.40 per million input/output tokens, against $10 / $30 for GPT-4 Turbo.
  • Qwen3.5-Flash accepts more context: 1M tokens versus 128K.

Side by side

GPT-4 TurboQwen3.5-Flash
ProviderOpenAIAlibaba (Qwen)
Noometry Index30.542.5
Released2023-11-062026-02-23
WeightsProprietaryProprietary
Context window128K1M
Max output4K66K
Input $ / M tokens$10$0.10
Output $ / M tokens$30$0.40
Results tracked3632

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Category by category

Coding Too close to call

GPT-4 Turbo: 33.8 (#249), Qwen3.5-Flash: 34.2 (#242)

BenchmarkGPT-4 TurboQwen3.5-Flash
LMArena Coding12681412
LMArena WebDev—1244
WeirdML18%—
BigCodeBench Instruct48.2%—
BigCodeBench Complete58.2%—
ALE-Bench—221.8
HumanEval+86.6%—
MBPP+73.3%—

Agentic & Tool Use Not comparable

GPT-4 Turbo: —, Qwen3.5-Flash: —

BenchmarkGPT-4 TurboQwen3.5-Flash
METR Time Horizons36.7%—
Vending-Bench 2—462.69

Reasoning Qwen3.5-Flash leads

GPT-4 Turbo: 15.3 (#317), Qwen3.5-Flash: 33.7 (#72)

BenchmarkGPT-4 TurboQwen3.5-Flash
Chess Puzzles6%21%
LMArena Hard Prompts12511403
DTBench61.6%82.9%
LMCA9.8%29.1%
Epoch Capabilities Index127.25143.98
SimpleBench25.1%—
Mystery Game Puzzles—20%
ForecastBench59.4—

Math Qwen3.5-Flash leads

GPT-4 Turbo: 9.0 (#322), Qwen3.5-Flash: 37.4 (#158)

BenchmarkGPT-4 TurboQwen3.5-Flash
FrontierMath (Tiers 1-3)0.7%18.2%
OTIS Mock AIME 2024-20256.7%84.4%
LMArena Math12721407
MATH Level 546.7%—
FrontierMath (Feb 2025 set)—6.2%
FrontierMath Tier 4 (v1)—0%

Knowledge Qwen3.5-Flash leads

GPT-4 Turbo: 24.3 (#268), Qwen3.5-Flash: 43.2 (#93)

BenchmarkGPT-4 TurboQwen3.5-Flash
GPQA Diamond46.6%82.3%
LMArena Expert12231407
SimpleQA Verified—20.3%
Confabulations28.4%—
Vectara Hallucination Rate—10.5%
MMLU81.3%—

Multimodal Not comparable

GPT-4 Turbo: 30.6 (#110), Qwen3.5-Flash: —

BenchmarkGPT-4 TurboQwen3.5-Flash
LMArena Vision1090—

Multilingual Qwen3.5-Flash leads

GPT-4 Turbo: 40.5 (#216), Qwen3.5-Flash: 50.5 (#121)

BenchmarkGPT-4 TurboQwen3.5-Flash
LMArena Non-English12451385
LMArena Chinese12421446
LMArena French12761412
LMArena German12591390
LMArena Japanese11941368
LMArena Korean11871344
LMArena Russian12591379
LMArena Spanish12601400

Instruction Following Qwen3.5-Flash leads

GPT-4 Turbo: 65.8 (#216), Qwen3.5-Flash: 72.6 (#139)

BenchmarkGPT-4 TurboQwen3.5-Flash
LMArena Instruction Following12491374

Long Context Qwen3.5-Flash leads

GPT-4 Turbo: 38.0 (#206), Qwen3.5-Flash: 42.4 (#124)

BenchmarkGPT-4 TurboQwen3.5-Flash
LMArena Longer Query12541392

Writing & Preference Qwen3.5-Flash leads

GPT-4 Turbo: 47.7 (#206), Qwen3.5-Flash: 57.9 (#122)

BenchmarkGPT-4 TurboQwen3.5-Flash
LMArena Text12721397
LMArena Creative Writing12691343
LMArena Multi-Turn12671393

Frequently asked questions

Is GPT-4 Turbo better than Qwen3.5-Flash?

Qwen3.5-Flash is the stronger model overall, scoring 42.5 to 30.5 on the Noometry Index.

Which is cheaper, GPT-4 Turbo or Qwen3.5-Flash?

Qwen3.5-Flash is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; GPT-4 Turbo lists at $10 and $30.

Is GPT-4 Turbo or Qwen3.5-Flash better for coding?

They score almost the same on coding (33.8 vs 34.2); test both on your own repository before choosing.

Which has the bigger context window?

Qwen3.5-Flash does, with 1M tokens against 128K.

How many benchmarks do GPT-4 Turbo and Qwen3.5-Flash share?

24 benchmarks have published results for both models. GPT-4 Turbo has 36 scored results on Noometry and Qwen3.5-Flash has 32.

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