Llama 3.1 70B Instruct vs Qwen3.6 27B
Qwen3.6 27B leads Llama 3.1 70B Instruct on the Artificial Analysis Intelligence Index, 21.4 to 6.6, and Llama 3.1 70B Instruct costs 4.8x less per output token ($0.56 vs $2.70 per million). Updated October 22, 2026; 11 benchmarks have a result for both.
Llama 3.1 70B Instruct or Qwen3.6 27B: which to pick
Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; Llama 3.1 70B Instruct has none, so the two cannot be ranked on coding.
Pick Llama 3.1 70B Instruct for output tokens 4.8x cheaper ($0.56 vs $2.70 per million).
- Shared results
- 11
- Llama 3.1 70B Instruct only
- 36
- Qwen3.6 27B only
- 13
- 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
Only Qwen3.6 27B has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.6 27B
Qwen3.6 27B leads, 21.4 against 6.6.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.6 27B has a AA Agentic Index result, 18.5.
Directional only
Long documents
Listed context window
Qwen3.6 27B
Qwen3.6 27B takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.1 70B Instruct
Llama 3.1 70B Instruct $0.0008, Qwen3.6 27B $0.0018 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.6 27B has the number, $1.30.
Directional only
At a glance
- Coding index
- Llama 3.1 70B Instruct not benchmarked, Qwen3.6 27B 36.8 from 2 benchmarks.
- Widest category
- Math, 87.5 points apart on average over 1 shared benchmark.
- Widest single gap
- OTIS Mock AIME: Llama 3.1 70B Instruct 3.6% against Qwen3.6 27B 91.1%, 87.5 points apart on a 0 to 100 scale.
- Output price
- Llama 3.1 70B Instruct $0.56 against Qwen3.6 27B $2.70 per million output tokens, 4.8x apart.
- Context window
- Llama 3.1 70B Instruct takes 131K tokens, Qwen3.6 27B 262K.
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 | Qwen3.6 27B | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 1 | ||
| General | 0 | 3 | ||
| All shared boards | 0 | 11 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Llama 3.1 70B Instruct | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 53.7 | Only Qwen3.6 27B measured |
| SWE-bench Verified (Vals AI) | Not measured | 70.0% | Only Qwen3.6 27B measured |
| Vibe Code Bench (Vals AI) | Not measured | 11.9% | Only Qwen3.6 27B measured |
| LiveCodeBench (Artificial Analysis run) | 23.2% | Not measured | Only Llama 3.1 70B Instruct measured |
| SciCode | Not measured | 42.8% | Only Qwen3.6 27B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 3.0% | 34.8% | Qwen3.6 27B |
| WeirdML | 9.0% | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: coding | 1260 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: software and IT | 1260 | Not measured | Only Llama 3.1 70B Instruct measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Llama 3.1 70B Instruct | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.6 | 21.4 | Qwen3.6 27B |
| Epoch Capabilities Index | 125.9 | 146.2 | Qwen3.6 27B |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.1 70B Instruct | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 18.5 | Only Qwen3.6 27B measured |
| τ²-Bench Telecom | 15.2% | 94.2% | Qwen3.6 27B |
| BALROG | 27.9% | Not measured | Only Llama 3.1 70B Instruct measured |
| TheAgentCompany | 6.9% | Not measured | Only Llama 3.1 70B Instruct measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Llama 3.1 70B Instruct | Qwen3.6 27B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 40.9% | 84.2% | Qwen3.6 27B |
| Humanity's Last Exam (Artificial Analysis run) | 4.5% | 23.1% | Qwen3.6 27B |
| GPQA Diamond (Epoch AI run) | 44.2% | 85.9% | Qwen3.6 27B |
| Mystery game puzzles | Not measured | 7.0% | Only Qwen3.6 27B measured |
| LMArena Text: hard prompts | 1241 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: expert | 1209 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: hard prompts in English | 1261 | Not measured | Only Llama 3.1 70B Instruct measured |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.1 70B Instruct | Qwen3.6 27B | 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% | 91.1% | Qwen3.6 27B |
| FrontierMath, tiers 1 to 3 | Not measured | 35.1% | Only Qwen3.6 27B measured |
| MATH level 5 | 36.7% | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: math | 1252 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: mathematical work | 1253 | Not measured | Only Llama 3.1 70B Instruct measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | Llama 3.1 70B Instruct | Qwen3.6 27B | Leader |
|---|---|---|---|
| CaseLaw (Vals AI) | Not measured | 53.2% | Only Qwen3.6 27B measured |
| CorpFin (Vals AI) | Not measured | 62.3% | Only Qwen3.6 27B measured |
| TaxEval (Vals AI) | Not measured | 71.3% | Only Qwen3.6 27B measured |
| MortgageTax (Vals AI) | Not measured | 68.3% | Only Qwen3.6 27B measured |
| MMLU Pro (Artificial Analysis run) | 67.6% | Not measured | Only Llama 3.1 70B Instruct measured |
| MMLU | 80.1% | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: science | 1276 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: medicine and healthcare | 1257 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: legal and government | 1283 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: business and finance | 1235 | Not measured | Only Llama 3.1 70B Instruct measured |
| Neither model has a result on the other 8 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.1 70B Instruct | Qwen3.6 27B | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Llama 3.1 70B Instruct | Qwen3.6 27B | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 45.6% | Only Qwen3.6 27B measured |
| IFBench | 34.4% | 67.6% | Qwen3.6 27B |
| Long context reasoning (AA-LCR) | Not measured | 77.3% | Only Qwen3.6 27B measured |
| DTBench | 60.0% | 78.1% | Qwen3.6 27B |
| LMCA | 14.8% | 34.5% | Qwen3.6 27B |
| LMArena Text | 1261 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: instruction following | 1231 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: creative writing | 1232 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: English | 1294 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: non-English | 1219 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: Chinese | 1215 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: German | 1222 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: Japanese | 1132 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: Korean | 1140 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: French | 1262 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: Spanish | 1253 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: Russian | 1234 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: multi-turn | 1256 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: longer queries | 1241 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: writing and language | 1236 | Not measured | Only Llama 3.1 70B Instruct measured |
| LMArena Text: entertainment and media | 1230 | Not measured | Only Llama 3.1 70B Instruct measured |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.1 70B Instruct | Qwen3.6 27B | 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
Qwen3.6 27B 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 | Qwen3.6 27B |
|---|---|---|
| Maker | Meta Llama | Qwen |
| Weights | Open weights | Open weights |
| Released | July 23, 2024 | April 27, 2026 |
| Context window | 131K | 262K |
| Input $/Mtok | $0.56 | $0.45 |
| Output $/Mtok | $0.56 | $2.70 |
| Coding index | Not benchmarked | 36.8 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | $1.30 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Llama 3.1 70B Instruct better than Qwen3.6 27B for coding?
Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; Llama 3.1 70B Instruct has none, so the two cannot be ranked on coding. Pick Llama 3.1 70B Instruct for output tokens 4.8x cheaper ($0.56 vs $2.70 per million).
Which is cheaper, Llama 3.1 70B Instruct or Qwen3.6 27B?
Llama 3.1 70B Instruct costs $0.56 per million input tokens and $0.56 per million output tokens; Qwen3.6 27B costs $0.45 and $2.70. Cost per solved task is known for Qwen3.6 27B only.
Which has the bigger context window, Llama 3.1 70B Instruct or Qwen3.6 27B?
Qwen3.6 27B, with 262K tokens against 131K.
How many benchmarks is the Llama 3.1 70B Instruct vs Qwen3.6 27B comparison based on?
11 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 Qwen3.6 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.