Kimi K2 0711 vs Llama 3.1 8B Instruct
Kimi K2 0711 leads Llama 3.1 8B Instruct on the Artificial Analysis Intelligence Index, 12.7 to 6.9, and Llama 3.1 8B Instruct costs 28.7x less per output token ($0.08 vs $2.30 per million). Updated October 21, 2026; 13 benchmarks have a result for both.
Kimi K2 0711 or Llama 3.1 8B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 13
- Kimi K2 0711 only
- 23
- Llama 3.1 8B Instruct only
- 37
- Like-for-like categories
- 7 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
Kimi K2 0711
Kimi K2 0711 leads, 12.7 against 6.9.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Kimi K2 0711 has a GPQA Diamond result, 71.5%.
Directional only
Math
AIME
No call
Only Kimi K2 0711 has a AIME result, 62.7%.
Directional only
Knowledge
MMLU Pro
No call
Only Kimi K2 0711 has a MMLU Pro result, 79.4%.
Directional only
Web design
Design Arena: websites
No call
Only Kimi K2 0711 has a Design Arena: websites result, 1050.
Directional only
Long documents
Listed context window
No call
Both take 131K tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.1 8B Instruct
Kimi K2 0711 $0.0017, Llama 3.1 8B Instruct $0.0001 per turn.
List prices
At a glance
- Coding index
- Kimi K2 0711 not benchmarked, Llama 3.1 8B Instruct not benchmarked.
- Widest category
- Math, 52.7 points apart on average over 2 shared benchmarks.
- Widest single gap
- AA Math Index: Kimi K2 0711 57.0 against Llama 3.1 8B Instruct 4.3, 52.7 points apart on a 0 to 100 scale.
- Output price
- Kimi K2 0711 $2.30 against Llama 3.1 8B Instruct $0.08 per million output tokens, 28.7x apart.
- Context window
- Both take 131K 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 | Kimi K2 0711 | Bars | Llama 3.1 8B Instruct | Ties |
|---|---|---|---|---|
| Coding | 3 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 2 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 1 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 13 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index30
| Benchmark | Kimi K2 0711 | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| Aider polyglot | 59.1% | Not measured | Only Kimi K2 0711 measured |
| SWE-bench Verified | 71.2% | Not measured | Only Kimi K2 0711 measured |
| AA Coding Index | Not measured | 5.4 | Only Llama 3.1 8B Instruct measured |
| LiveCodeBench (Vals AI) | 70.5% | Not measured | Only Kimi K2 0711 measured |
| LiveCodeBench (Artificial Analysis run) | 55.6% | 11.6% | Kimi K2 0711 |
| Terminal-Bench Hard (Artificial Analysis run) | 15.9% | 0.8% | Kimi K2 0711 |
| Terminal-Bench | 27.8% | Not measured | Only Kimi K2 0711 measured |
| GSO-Bench | 4.9% | Not measured | Only Kimi K2 0711 measured |
| WeirdML | 39.4% | 1.7% | Kimi K2 0711 |
| LMArena Text: coding | Not measured | 1195 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: software and IT | Not measured | 1195 | Only Llama 3.1 8B Instruct measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Kimi K2 0711 | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.7 | 6.9 | Kimi K2 0711 |
| Epoch Capabilities Index | 140.0 | 116.5 | Kimi K2 0711 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Kimi K2 0711 | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 61.1% | 16.4% | Kimi K2 0711 |
| BALROG | Not measured | 15.1% | Only Llama 3.1 8B Instruct measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Kimi K2 0711 | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 71.5% | Not measured | Only Kimi K2 0711 measured |
| GPQA Diamond (Artificial Analysis run) | 76.6% | 25.9% | Kimi K2 0711 |
| Humanity's Last Exam (Artificial Analysis run) | 7.4% | 5.3% | Kimi K2 0711 |
| GPQA Diamond (Epoch AI run) | Not measured | 27.0% | Only Llama 3.1 8B Instruct measured |
| SimpleBench | 26.3% | Not measured | Only Kimi K2 0711 measured |
| LMArena Text: hard prompts | Not measured | 1175 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: expert | Not measured | 1144 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: hard prompts in English | Not measured | 1196 | Only Llama 3.1 8B Instruct measured |
| Neither model has a result on the other 13 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Kimi K2 0711 | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| AIME | 62.7% | Not measured | Only Kimi K2 0711 measured |
| MATH 500 (Vals AI) | 94.2% | Not measured | Only Kimi K2 0711 measured |
| MGSM (Vals AI) | 91.0% | Not measured | Only Kimi K2 0711 measured |
| AA Math Index | 57.0 | 4.3 | Kimi K2 0711 |
| AIME 2025 (Artificial Analysis run) | 57.0% | 4.3% | Kimi K2 0711 |
| OTIS Mock AIME | Not measured | 1.7% | Only Llama 3.1 8B Instruct measured |
| MATH level 5 | Not measured | 22.9% | Only Llama 3.1 8B Instruct measured |
| GSM8K | Not measured | 82.4% | Only Llama 3.1 8B Instruct measured |
| LMArena Text: math | Not measured | 1179 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: mathematical work | Not measured | 1180 | Only Llama 3.1 8B Instruct measured |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Kimi K2 0711 | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| MMLU Pro | 79.4% | Not measured | Only Kimi K2 0711 measured |
| LegalBench (Vals AI) | 81.5% | Not measured | Only Kimi K2 0711 measured |
| CorpFin (Vals AI) | 50.4% | Not measured | Only Kimi K2 0711 measured |
| TaxEval (Vals AI) | 70.2% | Not measured | Only Kimi K2 0711 measured |
| MedQA (Vals AI) | 84.0% | Not measured | Only Kimi K2 0711 measured |
| MMLU Pro (Artificial Analysis run) | 82.4% | 47.6% | Kimi K2 0711 |
| MMLU | Not measured | 56.1% | Only Llama 3.1 8B Instruct measured |
| LMArena Text: science | Not measured | 1193 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: medicine and healthcare | Not measured | 1182 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: legal and government | Not measured | 1206 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: business and finance | Not measured | 1166 | Only Llama 3.1 8B Instruct measured |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Kimi K2 0711 | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1050 | Not measured | Only Kimi K2 0711 measured |
| Design Arena: UI components | 1030 | Not measured | Only Kimi K2 0711 measured |
| Design Arena: game dev | 974 | Not measured | Only Kimi K2 0711 measured |
| Design Arena: data viz | 1018 | Not measured | Only Kimi K2 0711 measured |
| Design Arena: all code categories | 1033 | Not measured | Only Kimi K2 0711 measured |
| Neither model has a result on the other 4 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Kimi K2 0711 | Llama 3.1 8B Instruct | Leader |
|---|---|---|---|
| IFBench | 41.5% | 28.6% | Kimi K2 0711 |
| Long context reasoning (AA-LCR) | 53.0% | 18.0% | Kimi K2 0711 |
| DTBench | Not measured | 50.9% | Only Llama 3.1 8B Instruct measured |
| LMCA | Not measured | 5.4% | Only Llama 3.1 8B Instruct measured |
| Fiction.LiveBench | 61.1% | Not measured | Only Kimi K2 0711 measured |
| Creative writing (Lech Mazur) | 85.6% | Not measured | Only Kimi K2 0711 measured |
| CL-bench | 11.9% | Not measured | Only Kimi K2 0711 measured |
| PIQA | Not measured | 81.2% | Only Llama 3.1 8B Instruct measured |
| LMArena Text | Not measured | 1187 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: instruction following | Not measured | 1159 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: creative writing | Not measured | 1154 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: English | Not measured | 1218 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: non-English | Not measured | 1148 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: Chinese | Not measured | 1151 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: German | Not measured | 1144 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: Japanese | Not measured | 1061 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: Korean | Not measured | 1053 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: French | Not measured | 1178 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: Spanish | Not measured | 1169 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: Russian | Not measured | 1158 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: multi-turn | Not measured | 1172 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: longer queries | Not measured | 1181 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: writing and language | Not measured | 1162 | Only Llama 3.1 8B Instruct measured |
| LMArena Text: entertainment and media | Not measured | 1145 | Only Llama 3.1 8B Instruct measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Kimi K2 0711 | Llama 3.1 8B 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
Llama 3.1 8B Instruct costs less
Repository review
50K input + 3K output tokens
Llama 3.1 8B Instruct costs less
Agent loop
200K input + 10K output tokens
Llama 3.1 8B 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 | MoonshotAI Kimi K2 0711 | Meta Llama 3.1 8B Instruct |
|---|---|---|
| Maker | Moonshot AI | Meta Llama |
| Weights | Open weights | Open weights |
| Released | July 11, 2025 | July 23, 2024 |
| Context window | 131K | 131K |
| Input $/Mtok | $0.57 | $0.05 |
| Output $/Mtok | $2.30 | $0.08 |
| 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 Kimi K2 0711 better than Llama 3.1 8B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Kimi K2 0711 or Llama 3.1 8B Instruct?
Kimi K2 0711 costs $0.57 per million input tokens and $2.30 per million output tokens; Llama 3.1 8B Instruct costs $0.05 and $0.08. Cost per solved task is known for neither of them.
Which has the bigger context window, Kimi K2 0711 or Llama 3.1 8B Instruct?
Neither: both take 131K tokens.
How many benchmarks is the Kimi K2 0711 vs Llama 3.1 8B Instruct comparison based on?
13 benchmarks have a result for both models as of October 21, 2026. Kimi K2 0711's coding index rests on 0 independent benchmarks and Llama 3.1 8B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.