GPT-4o-mini (2024-07-18) vs Qwen2.5 VL 72B Instruct
GPT-4o-mini (2024-07-18) costs 1.7x less per output token than Qwen2.5 VL 72B Instruct ($0.60 vs $1.00 per million). Updated October 22, 2026; 4 benchmarks have a result for both.
GPT-4o-mini (2024-07-18) 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 GPT-4o-mini (2024-07-18) for output tokens 1.7x cheaper ($0.60 vs $1.00 per million) and work measured by LMArena Vision or LMArena Vision: English, where it leads.
- Shared results
- 4
- GPT-4o-mini (2024-07-18) only
- 42
- Qwen2.5 VL 72B Instruct only
- 2
- Like-for-like categories
- 2 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
Long documents
Listed context window
No call
Both take 128K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4o-mini (2024-07-18)
GPT-4o-mini (2024-07-18) $0.0004, Qwen2.5 VL 72B Instruct $0.0013 per turn.
List prices
At a glance
- Coding index
- GPT-4o-mini (2024-07-18) not benchmarked, Qwen2.5 VL 72B Instruct not benchmarked.
- Widest category
- General, 8.8 points apart on average over 3 shared benchmarks.
- Widest single gap
- LMArena Vision: Chinese: GPT-4o-mini (2024-07-18) 1033 against Qwen2.5 VL 72B Instruct 1107, 12.3 points apart on a 0 to 100 scale.
- Output price
- GPT-4o-mini (2024-07-18) $0.60 against Qwen2.5 VL 72B Instruct $1.00 per million output tokens, 1.7x apart.
- Context window
- Both take 128K tokens.
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 | GPT-4o-mini (2024-07-18) | Bars | Qwen2.5 VL 72B Instruct | Ties |
|---|---|---|---|---|
| Knowledge | 1 | 0 | ||
| General | 0 | 3 | ||
| All shared boards | 1 | 3 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| Aider polyglot | 3.6% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| WeirdML | 11.8% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: coding | 1290 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: software and IT | 1291 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Neither model has a result on the other 39 boards here | |||
IntelligenceNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 2 boards here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| BALROG | 17.4% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| OSWorld | Not measured | 5.0% | Only Qwen2.5 VL 72B Instruct measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Epoch AI run) | 37.7% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| SimpleBench | 10.7% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Chess puzzles | 3.0% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Mystery game puzzles | 12.0% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| VPCT | 34.0% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: hard prompts | 1267 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: expert | 1234 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: hard prompts in English | 1278 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Kagi LLM Benchmark | Not measured | 36.0% | Only Qwen2.5 VL 72B Instruct measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| OTIS Mock AIME | 6.9% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| FrontierMath, tiers 1 to 3 | 0.7% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| MATH level 5 | 52.6% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: math | 1267 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: mathematical work | 1271 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| SimpleQA Verified | 8.3% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| MMLU | 76.7% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| GeoBench | 64.0% | 62.0% | GPT-4o-mini (2024-07-18) |
| LMArena Text: science | 1286 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: medicine and healthcare | 1282 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: legal and government | 1305 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: business and finance | 1265 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| Neither model has a result on the other 13 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-4o-mini (2024-07-18) | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| Creative writing (Lech Mazur) | 67.2% | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text | 1286 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: instruction following | 1258 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: creative writing | 1268 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Vision | 1066 | 1107 | Qwen2.5 VL 72B Instruct |
| LMArena Text: English | 1301 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: non-English | 1266 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: Chinese | 1265 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: German | 1272 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: Japanese | 1216 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: Korean | 1195 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: French | 1297 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: Spanish | 1276 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: Russian | 1275 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: Polish | 1244 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: multi-turn | 1285 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: longer queries | 1289 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: writing and language | 1282 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Text: entertainment and media | 1253 | Not measured | Only GPT-4o-mini (2024-07-18) measured |
| LMArena Vision: English | 1072 | 1115 | Qwen2.5 VL 72B Instruct |
| LMArena Vision: Chinese | 1033 | 1107 | Qwen2.5 VL 72B Instruct |
| Neither model has a result on the other 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o-mini (2024-07-18) | 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
GPT-4o-mini (2024-07-18) costs less
Repository review
50K input + 3K output tokens
GPT-4o-mini (2024-07-18) costs less
Agent loop
200K input + 10K output tokens
GPT-4o-mini (2024-07-18) 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 GPT-4o-mini (2024-07-18) | Qwen2.5 VL 72B Instruct |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | July 18, 2024 | February 1, 2025 |
| Context window | 128K | 128K |
| Input $/Mtok | $0.15 | $0.80 |
| Output $/Mtok | $0.60 | $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 GPT-4o-mini (2024-07-18) 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 GPT-4o-mini (2024-07-18) for output tokens 1.7x cheaper ($0.60 vs $1.00 per million) and work measured by LMArena Vision or LMArena Vision: English, where it leads.
Which is cheaper, GPT-4o-mini (2024-07-18) or Qwen2.5 VL 72B Instruct?
GPT-4o-mini (2024-07-18) costs $0.15 per million input tokens and $0.60 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, GPT-4o-mini (2024-07-18) or Qwen2.5 VL 72B Instruct?
Neither: both take 128K tokens.
How many benchmarks is the GPT-4o-mini (2024-07-18) vs Qwen2.5 VL 72B Instruct comparison based on?
4 benchmarks have a result for both models as of October 22, 2026. GPT-4o-mini (2024-07-18)'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.