Model comparison
DeepSeek V4 Flash vs Qwen2.5-VL 72B Instruct
DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 29.4 on the Noometry Index.
Last verified . 1 shared benchmarks.
Summary
- They share 1 benchmark with published results for both. DeepSeek V4 Flash scores higher in 1 category and Qwen2.5-VL 72B Instruct in 0 categories; one gap is clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4 Flash leads 53.7 to 20.0.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 52.2% for DeepSeek V4 Flash and 36% for Qwen2.5-VL 72B Instruct.
- DeepSeek V4 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $2.80 / $8.40 for Qwen2.5-VL 72B Instruct.
- DeepSeek V4 Flash accepts more context: 1M tokens versus 131K.
Side by side
| DeepSeek V4 Flash | Qwen2.5-VL 72B Instruct | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 53.6 | 29.4 |
| Released | 2026-04-24 | 2024-09 |
| Weights | Open | Open |
| Context window | 1M | 131K |
| Max output | 393K | 8K |
| Input $ / M tokens | $0.15 | $2.80 |
| Output $ / M tokens | $0.60 | $8.40 |
| Results tracked | 41 | 6 |
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Category by category
Coding Not comparable
DeepSeek V4 Flash: 47.6 (#56), Qwen2.5-VL 72B Instruct: —
| Benchmark | DeepSeek V4 Flash | Qwen2.5-VL 72B Instruct |
|---|---|---|
| FrontierCode | 18.8% | — |
| LMArena WebDev | 1581 | — |
| SciCode | 49.9% | — |
| WeirdML | 63% | — |
| LMArena Coding | 1457 | — |
| ALE-Bench | 1,306 | — |
Agentic & Tool Use Not comparable
DeepSeek V4 Flash: —, Qwen2.5-VL 72B Instruct: 18.4 (#136)
| Benchmark | DeepSeek V4 Flash | Qwen2.5-VL 72B Instruct |
|---|---|---|
| OSWorld | — | 5% |
Reasoning DeepSeek V4 Flash leads
DeepSeek V4 Flash: 53.7 (#25), Qwen2.5-VL 72B Instruct: 20.0 (#228)
| Benchmark | DeepSeek V4 Flash | Qwen2.5-VL 72B Instruct |
|---|---|---|
| Kagi LLM Benchmark | 52.2% | 36% |
| ARC-AGI-2 | 61.4% | — |
| SimpleBench | 61.1% | — |
| NYT Connections (extended) | 89.6% | — |
| ARC-AGI-1 | 89% | — |
| CritPt | 16.6% | — |
| Chess Puzzles | 33% | — |
| LMArena Hard Prompts | 1443 | — |
| Mystery Game Puzzles | 34% | — |
| DTBench | 90.9% | — |
| LMCA | 41.7% | — |
| Epoch Capabilities Index | 154.51 | — |
Math Not comparable
DeepSeek V4 Flash: 60.0 (#31), Qwen2.5-VL 72B Instruct: —
| Benchmark | DeepSeek V4 Flash | Qwen2.5-VL 72B Instruct |
|---|---|---|
| FrontierMath (Tiers 1-3) | 57.5% | — |
| FrontierMath Tier 4 | 24.4% | — |
| MathArena Final-Answer Competitions | 76.5% | — |
| OTIS Mock AIME 2024-2025 | 94.4% | — |
| ProofBench | 56% | — |
| LMArena Math | 1427 | — |
Knowledge Not comparable
DeepSeek V4 Flash: 55.5 (#43), Qwen2.5-VL 72B Instruct: —
| Benchmark | DeepSeek V4 Flash | Qwen2.5-VL 72B Instruct |
|---|---|---|
| GPQA Diamond | 91% | — |
| SimpleQA Verified | 33.6% | — |
| LMArena Expert | 1441 | — |
Multimodal Not comparable
DeepSeek V4 Flash: —, Qwen2.5-VL 72B Instruct: 32.9 (#92)
| Benchmark | DeepSeek V4 Flash | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Vision | — | 1107 |
| Video-MME | — | 73.5% |
| GeoBench | — | 62% |
| SpatialViz-Bench | — | 33.3% |
Multilingual Not comparable
DeepSeek V4 Flash: 53.2 (#71), Qwen2.5-VL 72B Instruct: —
| Benchmark | DeepSeek V4 Flash | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Non-English | 1420 | — |
| LMArena Chinese | 1467 | — |
| LMArena French | 1438 | — |
| LMArena German | 1418 | — |
| LMArena Japanese | 1403 | — |
| LMArena Korean | 1383 | — |
| LMArena Russian | 1428 | — |
| LMArena Spanish | 1437 | — |
Instruction Following Not comparable
DeepSeek V4 Flash: 75.1 (#76), Qwen2.5-VL 72B Instruct: —
| Benchmark | DeepSeek V4 Flash | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Instruction Following | 1421 | — |
Long Context Not comparable
DeepSeek V4 Flash: 43.8 (#81), Qwen2.5-VL 72B Instruct: —
| Benchmark | DeepSeek V4 Flash | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Longer Query | 1435 | — |
Writing & Preference Not comparable
DeepSeek V4 Flash: 64.0 (#59), Qwen2.5-VL 72B Instruct: —
| Benchmark | DeepSeek V4 Flash | Qwen2.5-VL 72B Instruct |
|---|---|---|
| LMArena Text | 1432 | — |
| LMArena Creative Writing | 1403 | — |
| EQ-Bench Creative Writing | 1559 | — |
| LMArena Multi-Turn | 1449 | — |
Frequently asked questions
Is DeepSeek V4 Flash better than Qwen2.5-VL 72B Instruct?
DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 29.4 on the Noometry Index.
Which is cheaper, DeepSeek V4 Flash or Qwen2.5-VL 72B Instruct?
DeepSeek V4 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; Qwen2.5-VL 72B Instruct lists at $2.80 and $8.40.
Which has the bigger context window?
DeepSeek V4 Flash does, with 1M tokens against 131K.
How many benchmarks do DeepSeek V4 Flash and Qwen2.5-VL 72B Instruct share?
1 benchmark has published results for both models. DeepSeek V4 Flash has 41 scored results on Noometry and Qwen2.5-VL 72B Instruct has 6.