GPT-4o-mini vs Qwen-Plus
GPT-4o-mini costs 1.3x less per output token than Qwen-Plus ($0.60 vs $0.78 per million). Updated October 21, 2026; 2 benchmarks have a result for both.
GPT-4o-mini or Qwen-Plus: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-4o-mini for output tokens 1.3x cheaper ($0.60 vs $0.78 per million) and work measured by DTBench or LMCA, where it leads.
- Shared results
- 2
- GPT-4o-mini only
- 25
- Qwen-Plus only
- 26
- 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 GPT-4o-mini has a AA Intelligence Index result, 6.7.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-4o-mini has a GPQA Diamond result, 44.2%.
Directional only
Math
AIME
No call
Only GPT-4o-mini has a AIME result, 11.5%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-4o-mini has a MMLU Pro result, 62.7%.
Directional only
Long documents
Listed context window
Qwen-Plus
Qwen-Plus takes 1M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-4o-mini
GPT-4o-mini $0.0004, Qwen-Plus $0.0006 per turn.
List prices
At a glance
- Coding index
- GPT-4o-mini not benchmarked, Qwen-Plus not benchmarked.
- Widest category
- General, 20.1 points apart on average over 2 shared benchmarks.
- Widest single gap
- DTBench: GPT-4o-mini 54.4% against Qwen-Plus 81.1%, 26.7 points apart on a 0 to 100 scale.
- Output price
- GPT-4o-mini $0.60 against Qwen-Plus $0.78 per million output tokens, 1.3x apart.
- Context window
- GPT-4o-mini takes 128K tokens, Qwen-Plus 1M.
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 | Bars | Qwen-Plus | Ties |
|---|---|---|---|---|
| General | 0 | 2 | ||
| All shared boards | 0 | 2 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index5 boards, none shared
| Benchmark | GPT-4o-mini | Qwen-Plus | Leader |
|---|---|---|---|
| AA Coding Index | 11.4 | Not measured | Only GPT-4o-mini measured |
| LiveCodeBench (Vals AI) | 26.4% | Not measured | Only GPT-4o-mini measured |
| LiveCodeBench (Artificial Analysis run) | 23.4% | Not measured | Only GPT-4o-mini measured |
| LMArena Text: coding | Not measured | 1328 | Only Qwen-Plus measured |
| LMArena Text: software and IT | Not measured | 1343 | Only Qwen-Plus measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o-mini | Qwen-Plus | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.7 | Not measured | Only GPT-4o-mini measured |
| Epoch Capabilities Index | 126.5 | Not measured | Only GPT-4o-mini measured |
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o-mini | Qwen-Plus | Leader |
|---|---|---|---|
| Neither model has a result on 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-4o-mini | Qwen-Plus | Leader |
|---|---|---|---|
| GPQA Diamond | 44.2% | Not measured | Only GPT-4o-mini measured |
| MMMU Pro (Vals AI) | 56.6% | Not measured | Only GPT-4o-mini measured |
| GPQA Diamond (Artificial Analysis run) | 42.6% | Not measured | Only GPT-4o-mini measured |
| Humanity's Last Exam (Artificial Analysis run) | 4.2% | Not measured | Only GPT-4o-mini measured |
| GPQA Diamond (Epoch AI run) | Not measured | 48.1% | Only Qwen-Plus measured |
| ARC-AGI-2 | 0.0% | Not measured | Only GPT-4o-mini measured |
| LMArena Text: hard prompts | Not measured | 1317 | Only Qwen-Plus measured |
| LMArena Text: expert | Not measured | 1328 | Only Qwen-Plus measured |
| LMArena Text: hard prompts in English | Not measured | 1333 | Only Qwen-Plus measured |
| Neither model has a result on the other 12 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | GPT-4o-mini | Qwen-Plus | Leader |
|---|---|---|---|
| AIME | 11.5% | Not measured | Only GPT-4o-mini measured |
| MATH 500 (Vals AI) | 72.6% | Not measured | Only GPT-4o-mini measured |
| MGSM (Vals AI) | 86.2% | Not measured | Only GPT-4o-mini measured |
| AA Math Index | 14.7 | Not measured | Only GPT-4o-mini measured |
| AIME 2025 (Artificial Analysis run) | 14.7% | Not measured | Only GPT-4o-mini measured |
| OTIS Mock AIME | Not measured | 17.8% | Only Qwen-Plus measured |
| MATH level 5 | Not measured | 65.3% | Only Qwen-Plus measured |
| GSM8K | 91.3% | Not measured | Only GPT-4o-mini measured |
| LMArena Text: math | Not measured | 1326 | Only Qwen-Plus measured |
| LMArena Text: mathematical work | Not measured | 1332 | Only Qwen-Plus measured |
| Neither model has a result on the other 3 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | GPT-4o-mini | Qwen-Plus | Leader |
|---|---|---|---|
| MMLU Pro | 62.7% | Not measured | Only GPT-4o-mini measured |
| CorpFin (Vals AI) | 45.5% | Not measured | Only GPT-4o-mini measured |
| TaxEval (Vals AI) | 60.5% | Not measured | Only GPT-4o-mini measured |
| MortgageTax (Vals AI) | 54.5% | Not measured | Only GPT-4o-mini measured |
| MedQA (Vals AI) | 72.4% | Not measured | Only GPT-4o-mini measured |
| MMLU Pro (Artificial Analysis run) | 64.8% | Not measured | Only GPT-4o-mini measured |
| MMLU | 81.8% | Not measured | Only GPT-4o-mini measured |
| LMArena Text: science | Not measured | 1352 | Only Qwen-Plus measured |
| LMArena Text: medicine and healthcare | Not measured | 1350 | Only Qwen-Plus measured |
| LMArena Text: legal and government | Not measured | 1349 | Only Qwen-Plus measured |
| LMArena Text: business and finance | Not measured | 1303 | Only Qwen-Plus measured |
| Neither model has a result on the other 6 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o-mini | Qwen-Plus | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4o-mini | Qwen-Plus | Leader |
|---|---|---|---|
| IFBench | 31.0% | Not measured | Only GPT-4o-mini measured |
| DTBench | 54.4% | 81.1% | Qwen-Plus |
| LMCA | 10.4% | 24.0% | Qwen-Plus |
| PIQA | 88.7% | Not measured | Only GPT-4o-mini measured |
| LMArena Text | Not measured | 1326 | Only Qwen-Plus measured |
| LMArena Text: instruction following | Not measured | 1303 | Only Qwen-Plus measured |
| LMArena Text: creative writing | Not measured | 1292 | Only Qwen-Plus measured |
| LMArena Text: English | Not measured | 1339 | Only Qwen-Plus measured |
| LMArena Text: non-English | Not measured | 1310 | Only Qwen-Plus measured |
| LMArena Text: Chinese | Not measured | 1347 | Only Qwen-Plus measured |
| LMArena Text: Japanese | Not measured | 1251 | Only Qwen-Plus measured |
| LMArena Text: Russian | Not measured | 1323 | Only Qwen-Plus measured |
| LMArena Text: multi-turn | Not measured | 1336 | Only Qwen-Plus measured |
| LMArena Text: longer queries | Not measured | 1324 | Only Qwen-Plus measured |
| LMArena Text: writing and language | Not measured | 1296 | Only Qwen-Plus measured |
| LMArena Text: entertainment and media | Not measured | 1270 | Only Qwen-Plus measured |
| Neither model has a result on the other 30 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o-mini | Qwen-Plus | 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 costs less
Repository review
50K input + 3K output tokens
GPT-4o-mini costs less
Agent loop
200K input + 10K output tokens
GPT-4o-mini 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 | Qwen-Plus |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | July 18, 2024 | February 1, 2025 |
| Context window | 128K | 1M |
| Input $/Mtok | $0.15 | $0.26 |
| Output $/Mtok | $0.60 | $0.78 |
| 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 better than Qwen-Plus for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4o-mini for output tokens 1.3x cheaper ($0.60 vs $0.78 per million) and work measured by DTBench or LMCA, where it leads.
Which is cheaper, GPT-4o-mini or Qwen-Plus?
GPT-4o-mini costs $0.15 per million input tokens and $0.60 per million output tokens; Qwen-Plus costs $0.26 and $0.78. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-4o-mini or Qwen-Plus?
Qwen-Plus, with 1M tokens against 128K.
How many benchmarks is the GPT-4o-mini vs Qwen-Plus comparison based on?
2 benchmarks have a result for both models as of October 21, 2026. GPT-4o-mini's coding index rests on 0 independent benchmarks and Qwen-Plus's on 0. Where only one model has a result, the page says so instead of naming a winner.