o3 Pro vs Qwen2.5 VL 72B Instruct
Qwen2.5 VL 72B Instruct costs 80.0x less per output token than o3 Pro ($1.00 vs $80.00 per million). Updated October 21, 2026; 1 benchmarks have a result for both.
o3 Pro 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 o3 Pro for a larger context window (200K against 128K tokens).
- Shared results
- 1
- o3 Pro only
- 11
- 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 o3 Pro has a AA Intelligence Index result, 21.9.
Directional only
Long documents
Listed context window
o3 Pro
o3 Pro takes 200K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 VL 72B Instruct
o3 Pro $0.060, Qwen2.5 VL 72B Instruct $0.0013 per turn.
List prices
At a glance
- Coding index
- o3 Pro not benchmarked, Qwen2.5 VL 72B Instruct not benchmarked.
- Widest category
- Reasoning, 36.1 points apart on average over 1 shared benchmark.
- Widest single gap
- Kagi LLM Benchmark: o3 Pro 72.1% against Qwen2.5 VL 72B Instruct 36.0%, 36.1 points apart on a 0 to 100 scale.
- Output price
- o3 Pro $80.00 against Qwen2.5 VL 72B Instruct $1.00 per million output tokens, 80.0x apart.
- Context window
- o3 Pro takes 200K 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 | o3 Pro | Bars | Qwen2.5 VL 72B Instruct | Ties |
|---|---|---|---|---|
| Reasoning | 1 | 0 | ||
| All shared boards | 1 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index2 boards, none shared
| Benchmark | o3 Pro | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| Aider polyglot | 84.9% | Not measured | Only o3 Pro measured |
| WeirdML | 58.2% | Not measured | Only o3 Pro measured |
| Neither model has a result on the other 37 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | o3 Pro | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 21.9 | Not measured | Only o3 Pro measured |
| Epoch Capabilities Index | 147.3 | Not measured | Only o3 Pro measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | o3 Pro | 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 | o3 Pro | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 84.5% | Not measured | Only o3 Pro measured |
| ARC-AGI | 59.3% | Not measured | Only o3 Pro measured |
| ARC-AGI-2 | 4.9% | Not measured | Only o3 Pro measured |
| Kagi LLM Benchmark | 72.1% | 36.0% | o3 Pro |
| Neither model has a result on the other 18 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | o3 Pro | 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 | o3 Pro | 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 designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | o3 Pro | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | o3 Pro | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| DTBench | 86.9% | Not measured | Only o3 Pro measured |
| LMCA | 38.5% | Not measured | Only o3 Pro measured |
| Fiction.LiveBench | 97.2% | Not measured | Only o3 Pro measured |
| Creative writing (Lech Mazur) | 84.4% | Not measured | Only o3 Pro 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 39 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | o3 Pro | 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
Qwen2.5 VL 72B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen2.5 VL 72B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen2.5 VL 72B Instruct 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 | OpenAI o3 Pro | Qwen2.5 VL 72B Instruct |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | June 10, 2025 | February 1, 2025 |
| Context window | 200K | 128K |
| Input $/Mtok | $20.00 | $0.80 |
| Output $/Mtok | $80.00 | $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 o3 Pro 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 o3 Pro for a larger context window (200K against 128K tokens).
Which is cheaper, o3 Pro or Qwen2.5 VL 72B Instruct?
o3 Pro costs $20.00 per million input tokens and $80.00 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, o3 Pro or Qwen2.5 VL 72B Instruct?
o3 Pro, with 200K tokens against 128K.
How many benchmarks is the o3 Pro vs Qwen2.5 VL 72B Instruct comparison based on?
1 benchmarks have a result for both models as of October 21, 2026. o3 Pro'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.