DeepSeek V3.1 vs Qwen2.5 VL 72B Instruct
Qwen2.5 VL 72B Instruct costs 1.5x less per output token than DeepSeek V3.1 ($1.00 vs $1.50 per million). Updated October 22, 2026; 1 benchmarks have a result for both.
DeepSeek V3.1 or Qwen2.5 VL 72B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V3.1 for a larger context window (164K against 128K tokens).
- Shared results
- 1
- DeepSeek V3.1 only
- 10
- Qwen2.5 VL 72B Instruct only
- 5
- Like-for-like categories
- 1 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only DeepSeek V3.1 has a AA Intelligence Index result, 13.7.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V3.1 has a Design Arena: websites result, 1122.
Directional only
Long documents
Listed context window
DeepSeek V3.1
DeepSeek V3.1 takes 164K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
No call
DeepSeek V3.1 $0.0013, Qwen2.5 VL 72B Instruct $0.0013 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.1 not benchmarked, Qwen2.5 VL 72B Instruct not benchmarked.
- Widest category
- Reasoning, 17.2 points apart on average over 1 shared benchmark.
- Widest single gap
- Kagi LLM Benchmark: DeepSeek V3.1 53.2% against Qwen2.5 VL 72B Instruct 36.0%, 17.2 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.1 $1.50 against Qwen2.5 VL 72B Instruct $1.00 per million output tokens, 1.5x apart.
- Context window
- DeepSeek V3.1 takes 164K tokens, Qwen2.5 VL 72B Instruct 128K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | DeepSeek V3.1 | Bars | Qwen2.5 VL 72B Instruct | Ties |
|---|---|---|---|---|
| Reasoning | 1 | 0 | ||
| All shared boards | 1 | 0 |
CodingNot comparableSix of these boards feed the Vibe Coding Index0 boards, none shared
| Benchmark | DeepSeek V3.1 | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 39 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V3.1 | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.7 | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V3.1 | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| OSWorld | Not measured | 5.0% | Only Qwen2.5 VL 72B Instruct measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3.1 | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| Kagi LLM Benchmark | 53.2% | 36.0% | DeepSeek V3.1 |
| Neither model has a result on the other 21 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 13 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V3.1 | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| GeoBench | Not measured | 62.0% | Only Qwen2.5 VL 72B Instruct measured |
| Neither model has a result on the other 17 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | DeepSeek V3.1 | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1122 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: UI components | 1083 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: game dev | 1084 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: data viz | 1096 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: 3D | 1077 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: all code categories | 1112 | Not measured | Only DeepSeek V3.1 measured |
| Design Arena: SVG | 967 | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | DeepSeek V3.1 | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| DTBench | 82.7% | Not measured | Only DeepSeek V3.1 measured |
| LMCA | 24.3% | Not measured | Only DeepSeek V3.1 measured |
| LMArena Vision | Not measured | 1107 | Only Qwen2.5 VL 72B Instruct measured |
| LMArena Vision: English | Not measured | 1115 | Only Qwen2.5 VL 72B Instruct measured |
| LMArena Vision: Chinese | Not measured | 1107 | Only Qwen2.5 VL 72B Instruct measured |
| Neither model has a result on the other 41 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Same cost
Repository review
50K input + 3K output tokens
DeepSeek V3.1 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3.1 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | DeepSeek V3.1 | Qwen2.5 VL 72B Instruct |
|---|---|---|
| Maker | DeepSeek | Qwen |
| Weights | Open weights | Open weights |
| Released | August 21, 2025 | February 1, 2025 |
| Context window | 164K | 128K |
| Input $/Mtok | $0.55 | $0.80 |
| Output $/Mtok | $1.50 | $1.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.1 better than Qwen2.5 VL 72B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.1 for a larger context window (164K against 128K tokens).
Which is cheaper, DeepSeek V3.1 or Qwen2.5 VL 72B Instruct?
DeepSeek V3.1 costs $0.55 per million input tokens and $1.50 per million output tokens; Qwen2.5 VL 72B Instruct costs $0.80 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3.1 or Qwen2.5 VL 72B Instruct?
DeepSeek V3.1, with 164K tokens against 128K.
How many benchmarks is the DeepSeek V3.1 vs Qwen2.5 VL 72B Instruct comparison based on?
1 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.1's coding index rests on 0 independent benchmarks and Qwen2.5 VL 72B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.