Llama 3.1 70B Instruct vs Qwen3 235B A22B Thinking 2507
Qwen3 235B A22B Thinking 2507 leads Llama 3.1 70B Instruct on the Artificial Analysis Intelligence Index, 12.7 to 6.6, and Llama 3.1 70B Instruct costs 6.2x less per output token ($0.56 vs $3.50 per million). Updated October 22, 2026; 32 benchmarks have a result for both.
Llama 3.1 70B Instruct or Qwen3 235B A22B Thinking 2507: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Llama 3.1 70B Instruct for output tokens 6.2x cheaper ($0.56 vs $3.50 per million) and a larger context window (131K against 128K tokens).
- Shared results
- 32
- Llama 3.1 70B Instruct only
- 15
- Qwen3 235B A22B Thinking 2507 only
- 14
- Like-for-like categories
- 6 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
Qwen3 235B A22B Thinking 2507
Qwen3 235B A22B Thinking 2507 leads, 12.7 against 6.6.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3 235B A22B Thinking 2507 has a AA Agentic Index result, 1.3.
Directional only
Web design
Design Arena: websites
No call
Only Qwen3 235B A22B Thinking 2507 has a Design Arena: websites result, 1052.
Directional only
Long documents
Listed context window
Llama 3.1 70B Instruct
Llama 3.1 70B Instruct takes 131K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.1 70B Instruct
Llama 3.1 70B Instruct $0.0008, Qwen3 235B A22B Thinking 2507 $0.0022 per turn.
List prices
At a glance
- Coding index
- Llama 3.1 70B Instruct not benchmarked, Qwen3 235B A22B Thinking 2507 not benchmarked.
- Widest category
- Math, 46.7 points apart on average over 3 shared benchmarks.
- Widest single gap
- OTIS Mock AIME: Llama 3.1 70B Instruct 3.6% against Qwen3 235B A22B Thinking 2507 86.7%, 83.1 points apart on a 0 to 100 scale.
- Output price
- Llama 3.1 70B Instruct $0.56 against Qwen3 235B A22B Thinking 2507 $3.50 per million output tokens, 6.2x apart.
- Context window
- Llama 3.1 70B Instruct takes 131K tokens, Qwen3 235B A22B Thinking 2507 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 | Llama 3.1 70B Instruct | Bars | Qwen3 235B A22B Thinking 2507 | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 4 | ||
| Math | 0 | 3 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 17 | ||
| All shared boards | 0 | 32 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | Llama 3.1 70B Instruct | Qwen3 235B A22B Thinking 2507 | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 22.1 | Only Qwen3 235B A22B Thinking 2507 measured |
| LiveCodeBench (Artificial Analysis run) | 23.2% | Not measured | Only Llama 3.1 70B Instruct measured |
| Terminal-Bench Hard (Artificial Analysis run) | 3.0% | Not measured | Only Llama 3.1 70B Instruct measured |
| WeirdML | 9.0% | 41.0% | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: coding | 1260 | 1424 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: software and IT | 1260 | 1431 | Qwen3 235B A22B Thinking 2507 |
| Neither model has a result on the other 37 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.1 70B Instruct | Qwen3 235B A22B Thinking 2507 | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.6 | 12.7 | Qwen3 235B A22B Thinking 2507 |
| Epoch Capabilities Index | 125.9 | Not measured | Only Llama 3.1 70B Instruct measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Llama 3.1 70B Instruct | Qwen3 235B A22B Thinking 2507 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.3 | Only Qwen3 235B A22B Thinking 2507 measured |
| τ²-Bench Telecom | 15.2% | Not measured | Only Llama 3.1 70B Instruct measured |
| BALROG | 27.9% | Not measured | Only Llama 3.1 70B Instruct measured |
| TheAgentCompany | 6.9% | Not measured | Only Llama 3.1 70B Instruct measured |
| Vending-Bench 2 | Not measured | -$11 | Only Qwen3 235B A22B Thinking 2507 measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Llama 3.1 70B Instruct | Qwen3 235B A22B Thinking 2507 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 40.9% | Not measured | Only Llama 3.1 70B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | 4.5% | Not measured | Only Llama 3.1 70B Instruct measured |
| GPQA Diamond (Epoch AI run) | 44.2% | 80.1% | Qwen3 235B A22B Thinking 2507 |
| Mystery game puzzles | Not measured | 9.0% | Only Qwen3 235B A22B Thinking 2507 measured |
| LMArena Text: hard prompts | 1241 | 1416 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: expert | 1209 | 1463 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: hard prompts in English | 1261 | 1432 | Qwen3 235B A22B Thinking 2507 |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Llama 3.1 70B Instruct | Qwen3 235B A22B Thinking 2507 | Leader |
|---|---|---|---|
| AA Math Index | 4.0 | Not measured | Only Llama 3.1 70B Instruct measured |
| AIME 2025 (Artificial Analysis run) | 4.0% | Not measured | Only Llama 3.1 70B Instruct measured |
| OTIS Mock AIME | 3.6% | 86.7% | Qwen3 235B A22B Thinking 2507 |
| MATH level 5 | 36.7% | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: math | 1252 | 1412 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: mathematical work | 1253 | 1435 | Qwen3 235B A22B Thinking 2507 |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Llama 3.1 70B Instruct | Qwen3 235B A22B Thinking 2507 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 67.6% | Not measured | Only Llama 3.1 70B Instruct measured |
| SimpleQA Verified | Not measured | 40.4% | Only Qwen3 235B A22B Thinking 2507 measured |
| MMLU | 80.1% | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: science | 1276 | 1433 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: medicine and healthcare | 1257 | 1456 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: legal and government | 1283 | 1415 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: business and finance | 1235 | 1413 | Qwen3 235B A22B Thinking 2507 |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | Llama 3.1 70B Instruct | Qwen3 235B A22B Thinking 2507 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1052 | Only Qwen3 235B A22B Thinking 2507 measured |
| Design Arena: UI components | Not measured | 942 | Only Qwen3 235B A22B Thinking 2507 measured |
| Design Arena: game dev | Not measured | 956 | Only Qwen3 235B A22B Thinking 2507 measured |
| Design Arena: data viz | Not measured | 950 | Only Qwen3 235B A22B Thinking 2507 measured |
| Design Arena: 3D | Not measured | 997 | Only Qwen3 235B A22B Thinking 2507 measured |
| Design Arena: all code categories | Not measured | 1032 | Only Qwen3 235B A22B Thinking 2507 measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index017
| Benchmark | Llama 3.1 70B Instruct | Qwen3 235B A22B Thinking 2507 | Leader |
|---|---|---|---|
| IFBench | 34.4% | Not measured | Only Llama 3.1 70B Instruct measured |
| DTBench | 60.0% | 80.3% | Qwen3 235B A22B Thinking 2507 |
| LMCA | 14.8% | 29.3% | Qwen3 235B A22B Thinking 2507 |
| Fiction.LiveBench | Not measured | 75.0% | Only Qwen3 235B A22B Thinking 2507 measured |
| Creative writing (Lech Mazur) | Not measured | 82.4% | Only Qwen3 235B A22B Thinking 2507 measured |
| LMArena Text | 1261 | 1415 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: instruction following | 1231 | 1386 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: creative writing | 1232 | 1384 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: English | 1294 | 1429 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: non-English | 1219 | 1397 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: Chinese | 1215 | 1481 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: German | 1222 | 1408 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: Japanese | 1132 | 1386 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: Korean | 1140 | 1375 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: French | 1262 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: Spanish | 1253 | 1389 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: Russian | 1234 | 1401 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: Polish | Not measured | 1390 | Only Qwen3 235B A22B Thinking 2507 measured |
| LMArena Text: multi-turn | 1256 | 1406 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: longer queries | 1241 | 1400 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: writing and language | 1236 | 1388 | Qwen3 235B A22B Thinking 2507 |
| LMArena Text: entertainment and media | 1230 | 1370 | Qwen3 235B A22B Thinking 2507 |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.1 70B Instruct | Qwen3 235B A22B Thinking 2507 | 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
Llama 3.1 70B Instruct costs less
Repository review
50K input + 3K output tokens
Llama 3.1 70B Instruct costs less
Agent loop
200K input + 10K output tokens
Llama 3.1 70B 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 | Meta Llama 3.1 70B Instruct | Qwen3 235B A22B Thinking 2507 |
|---|---|---|
| Maker | Meta Llama | Qwen |
| Weights | Open weights | Open weights |
| Released | July 23, 2024 | July 25, 2025 |
| Context window | 131K | 128K |
| Input $/Mtok | $0.56 | $0.45 |
| Output $/Mtok | $0.56 | $3.50 |
| 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 Llama 3.1 70B Instruct better than Qwen3 235B A22B Thinking 2507 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Llama 3.1 70B Instruct for output tokens 6.2x cheaper ($0.56 vs $3.50 per million) and a larger context window (131K against 128K tokens).
Which is cheaper, Llama 3.1 70B Instruct or Qwen3 235B A22B Thinking 2507?
Llama 3.1 70B Instruct costs $0.56 per million input tokens and $0.56 per million output tokens; Qwen3 235B A22B Thinking 2507 costs $0.45 and $3.50. Cost per solved task is known for neither of them.
Which has the bigger context window, Llama 3.1 70B Instruct or Qwen3 235B A22B Thinking 2507?
Llama 3.1 70B Instruct, with 131K tokens against 128K.
How many benchmarks is the Llama 3.1 70B Instruct vs Qwen3 235B A22B Thinking 2507 comparison based on?
32 benchmarks have a result for both models as of October 22, 2026. Llama 3.1 70B Instruct's coding index rests on 0 independent benchmarks and Qwen3 235B A22B Thinking 2507's on 0. Where only one model has a result, the page says so instead of naming a winner.