o1 vs Qwen-Plus
Qwen-Plus costs 76.9x less per output token than o1 ($0.78 vs $60.00 per million). Updated October 21, 2026; 28 benchmarks have a result for both.
o1 or Qwen-Plus: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick o1 for work measured by DTBench or LMCA, where it leads.
- Shared results
- 28
- o1 only
- 41
- Qwen-Plus only
- 0
- Like-for-like categories
- 5 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 o1 has a AA Intelligence Index result, 15.2.
Directional only
Reasoning
GPQA Diamond
No call
Only o1 has a GPQA Diamond result, 73.2%.
Directional only
Math
AIME
No call
Only o1 has a AIME result, 71.5%.
Directional only
Knowledge
MMLU Pro
No call
Only o1 has a MMLU Pro result, 83.5%.
Directional only
Long documents
Listed context window
Qwen-Plus
Qwen-Plus takes 1M tokens against 200K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen-Plus
o1 $0.045, Qwen-Plus $0.0006 per turn.
List prices
At a glance
- Coding index
- o1 not benchmarked, Qwen-Plus not benchmarked.
- Widest category
- Math, 26.2 points apart on average over 4 shared benchmarks.
- Widest single gap
- OTIS Mock AIME: o1 73.3% against Qwen-Plus 17.8%, 55.6 points apart on a 0 to 100 scale.
- Output price
- o1 $60.00 against Qwen-Plus $0.78 per million output tokens, 76.9x apart.
- Context window
- o1 takes 200K 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 | o1 | Bars | Qwen-Plus | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Reasoning | 4 | 0 | ||
| Math | 4 | 0 | ||
| Knowledge | 4 | 0 | ||
| General | 12 | 2 | ||
| All shared boards | 26 | 2 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | o1 | Qwen-Plus | Leader |
|---|---|---|---|
| Aider polyglot | 61.7% | Not measured | Only o1 measured |
| AA Coding Index | 39.7 | Not measured | Only o1 measured |
| LiveCodeBench (Vals AI) | 50.3% | Not measured | Only o1 measured |
| LiveCodeBench (Artificial Analysis run) | 67.9% | Not measured | Only o1 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 12.9% | Not measured | Only o1 measured |
| WeirdML | 46.1% | Not measured | Only o1 measured |
| LMArena Text: coding | 1367 | 1328 | o1 |
| LMArena Text: software and IT | 1366 | 1343 | o1 |
| SciCode | 4.6% | Not measured | Only o1 measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | o1 | Qwen-Plus | Leader |
|---|---|---|---|
| AA Intelligence Index | 15.2 | Not measured | Only o1 measured |
| Epoch Capabilities Index | 141.7 | Not measured | Only o1 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | o1 | Qwen-Plus | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 62.6% | Not measured | Only o1 measured |
| METR time horizons | 51.1% | Not measured | Only o1 measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | o1 | Qwen-Plus | Leader |
|---|---|---|---|
| GPQA Diamond | 73.2% | Not measured | Only o1 measured |
| MMMU Pro (Vals AI) | 77.4% | Not measured | Only o1 measured |
| GPQA Diamond (Artificial Analysis run) | 74.7% | Not measured | Only o1 measured |
| Humanity's Last Exam (Artificial Analysis run) | 7.0% | Not measured | Only o1 measured |
| GPQA Diamond (Epoch AI run) | 76.8% | 48.1% | o1 |
| ARC-AGI | 30.7% | Not measured | Only o1 measured |
| SimpleBench | 40.1% | Not measured | Only o1 measured |
| Chess puzzles | 10.0% | Not measured | Only o1 measured |
| VPCT | 37.0% | Not measured | Only o1 measured |
| LMArena Text: hard prompts | 1371 | 1317 | o1 |
| LMArena Text: expert | 1361 | 1328 | o1 |
| LMArena Text: hard prompts in English | 1372 | 1333 | o1 |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | o1 | Qwen-Plus | Leader |
|---|---|---|---|
| AIME | 71.5% | Not measured | Only o1 measured |
| MATH 500 (Vals AI) | 90.4% | Not measured | Only o1 measured |
| MGSM (Vals AI) | 89.3% | Not measured | Only o1 measured |
| OTIS Mock AIME | 73.3% | 17.8% | o1 |
| FrontierMath, tiers 1 to 3 | 14.7% | Not measured | Only o1 measured |
| MATH level 5 | 94.7% | 65.3% | o1 |
| LMArena Text: math | 1388 | 1326 | o1 |
| LMArena Text: mathematical work | 1390 | 1332 | o1 |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | o1 | Qwen-Plus | Leader |
|---|---|---|---|
| MMLU Pro | 83.5% | Not measured | Only o1 measured |
| LegalBench (Vals AI) | 80.4% | Not measured | Only o1 measured |
| TaxEval (Vals AI) | 74.3% | Not measured | Only o1 measured |
| MedQA (Vals AI) | 96.5% | Not measured | Only o1 measured |
| MMLU Pro (Artificial Analysis run) | 84.1% | Not measured | Only o1 measured |
| SimpleQA Verified | 41.1% | Not measured | Only o1 measured |
| GeoBench | 80.0% | Not measured | Only o1 measured |
| LMArena Text: science | 1369 | 1352 | o1 |
| LMArena Text: medicine and healthcare | 1355 | 1350 | o1 |
| LMArena Text: legal and government | 1368 | 1349 | o1 |
| LMArena Text: business and finance | 1328 | 1303 | o1 |
| Neither model has a result on the other 7 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | o1 | Qwen-Plus | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index122
| Benchmark | o1 | Qwen-Plus | Leader |
|---|---|---|---|
| IFBench | 70.3% | Not measured | Only o1 measured |
| Long context reasoning (AA-LCR) | 65.0% | Not measured | Only o1 measured |
| DTBench | 74.7% | 81.1% | Qwen-Plus |
| LMCA | 22.3% | 24.0% | Qwen-Plus |
| Fiction.LiveBench | 83.3% | Not measured | Only o1 measured |
| Creative writing (Lech Mazur) | 70.2% | Not measured | Only o1 measured |
| LMArena Text | 1366 | 1326 | o1 |
| LMArena Text: instruction following | 1367 | 1303 | o1 |
| LMArena Text: creative writing | 1347 | 1292 | o1 |
| LMArena Vision | 1168 | Not measured | Only o1 measured |
| LMArena Text: English | 1372 | 1339 | o1 |
| LMArena Text: non-English | 1358 | 1310 | o1 |
| LMArena Text: Chinese | 1394 | 1347 | o1 |
| LMArena Text: German | 1337 | Not measured | Only o1 measured |
| LMArena Text: Japanese | 1346 | 1251 | o1 |
| LMArena Text: Korean | 1396 | Not measured | Only o1 measured |
| LMArena Text: French | 1344 | Not measured | Only o1 measured |
| LMArena Text: Spanish | 1345 | Not measured | Only o1 measured |
| LMArena Text: Russian | 1356 | 1323 | o1 |
| LMArena Text: multi-turn | 1355 | 1336 | o1 |
| LMArena Text: longer queries | 1378 | 1324 | o1 |
| LMArena Text: writing and language | 1360 | 1296 | o1 |
| LMArena Text: entertainment and media | 1358 | 1270 | o1 |
| LMArena Vision: English | 1162 | Not measured | Only o1 measured |
| LMArena Vision: Chinese | 1235 | Not measured | Only o1 measured |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | o1 | 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
Qwen-Plus costs less
Repository review
50K input + 3K output tokens
Qwen-Plus costs less
Agent loop
200K input + 10K output tokens
Qwen-Plus 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 o1 | Qwen-Plus |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | December 17, 2024 | February 1, 2025 |
| Context window | 200K | 1M |
| Input $/Mtok | $15.00 | $0.26 |
| Output $/Mtok | $60.00 | $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 o1 better than Qwen-Plus for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick o1 for work measured by DTBench or LMCA, where it leads.
Which is cheaper, o1 or Qwen-Plus?
o1 costs $15.00 per million input tokens and $60.00 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, o1 or Qwen-Plus?
Qwen-Plus, with 1M tokens against 200K.
How many benchmarks is the o1 vs Qwen-Plus comparison based on?
28 benchmarks have a result for both models as of October 21, 2026. o1'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.