Model comparison
Kimi K2.6 vs Llama 3.1-70B
Kimi K2.6 is the stronger model overall, scoring 47.7 to 29.6 on the Noometry Index. Llama 3.1-70B costs 4.3× less per token, which makes it the better buy when Kimi K2.6's lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
MetaSummary
- They share 24 benchmarks with published results for both. Kimi K2.6 scores higher in 8 categories and Llama 3.1-70B in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Kimi K2.6 leads 57.0 to 13.5.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 96.1% for Kimi K2.6 and 3.6% for Llama 3.1-70B.
- Llama 3.1-70B is cheaper at $0.40 / $0.40 per million input/output tokens, against $0.95 / $4 for Kimi K2.6.
- Kimi K2.6 accepts more context: 262K tokens versus 128K.
Side by side
| Kimi K2.6 | Llama 3.1-70B | |
|---|---|---|
| Provider | Moonshot AI | Meta |
| Noometry Index | 47.7 | 29.6 |
| Released | 2026-04-20 | 2024-07-23 |
| Weights | Open | Open |
| Context window | 262K | 128K |
| Max output | 262K | 4K |
| Input $ / M tokens | $0.95 | $0.40 |
| Output $ / M tokens | $4 | $0.40 |
| Results tracked | 51 | 35 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Kimi K2.6 leads
Kimi K2.6: 50.7 (#43), Llama 3.1-70B: 30.3 (#296)
| Benchmark | Kimi K2.6 | Llama 3.1-70B |
|---|---|---|
| WeirdML | 55.9% | 9% |
| LMArena Coding | 1488 | 1260 |
| SWE-bench Verified | 76.7% | — |
| LMArena WebDev | 1509 | — |
| SciCode | 53.5% | — |
| BigCodeBench Instruct | — | 46.1% |
| BigCodeBench Complete | — | 54.8% |
| ALE-Bench | 1,093 | — |
Agentic & Tool Use Llama 3.1-70B leads
Kimi K2.6: 21.9 (#137), Llama 3.1-70B: 25.1 (#112)
| Benchmark | Kimi K2.6 | Llama 3.1-70B |
|---|---|---|
| OSWorld 2.0 | 4.6% | — |
| TheAgentCompany | — | 6.9% |
| BALROG | — | 27.9% |
| ExploitBench | 18.4% | — |
| GBAEval | 0.9% | — |
| GDP.pdf | 12% | — |
| Vending-Bench 2 | 6,205 | — |
Reasoning Kimi K2.6 leads
Kimi K2.6: 40.5 (#55), Llama 3.1-70B: 21.6 (#220)
| Benchmark | Kimi K2.6 | Llama 3.1-70B |
|---|---|---|
| LMArena Hard Prompts | 1470 | 1241 |
| DTBench | 90.9% | 60% |
| LMCA | 37.3% | 14.8% |
| Epoch Capabilities Index | 151.05 | 125.92 |
| NYT Connections (extended) | 87.2% | — |
| CritPt | 8% | — |
| Chess Puzzles | 26% | — |
| EBR-Bench | 2.4% | — |
| Mystery Game Puzzles | 18% | — |
Math Kimi K2.6 leads
Kimi K2.6: 57.0 (#41), Llama 3.1-70B: 13.5 (#304)
| Benchmark | Kimi K2.6 | Llama 3.1-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 96.1% | 3.6% |
| LMArena Math | 1475 | 1252 |
| FrontierMath (Tiers 1-3) | 57.2% | — |
| FrontierMath Tier 4 | 25.6% | — |
| MathArena Final-Answer Competitions | 72.9% | — |
| ProofBench | 16% | — |
| Omni-MATH | — | 21% |
| MATH Level 5 | — | 36.7% |
| FrontierMath (Feb 2025 set) | 39% | — |
| FrontierMath Tier 4 (v1) | 14.6% | — |
Knowledge Kimi K2.6 leads
Kimi K2.6: 54.0 (#54), Llama 3.1-70B: 24.2 (#269)
| Benchmark | Kimi K2.6 | Llama 3.1-70B |
|---|---|---|
| GPQA Diamond | 90.8% | 44.2% |
| LMArena Expert | 1491 | 1209 |
| SimpleQA Verified | 34.9% | — |
| MMLU-Pro | — | 65.3% |
| Vectara Hallucination Rate | 10.8% | — |
| GPQA (HELM) | — | 42.6% |
| MMLU | — | 80.1% |
Multimodal Not comparable
Kimi K2.6: 31.6 (#103), Llama 3.1-70B: —
| Benchmark | Kimi K2.6 | Llama 3.1-70B |
|---|---|---|
| LMArena Vision | 1283 | — |
| Blueprint-Bench 2 | 3.9% | — |
| Furniture Assembly | 21.7% | — |
| LMArena Document | 1451 | — |
Multilingual Kimi K2.6 leads
Kimi K2.6: 54.9 (#37), Llama 3.1-70B: 38.8 (#225)
| Benchmark | Kimi K2.6 | Llama 3.1-70B |
|---|---|---|
| LMArena Non-English | 1446 | 1219 |
| LMArena Chinese | 1521 | 1215 |
| LMArena French | 1471 | 1261 |
| LMArena German | 1450 | 1222 |
| LMArena Japanese | 1443 | 1132 |
| LMArena Korean | 1427 | 1140 |
| LMArena Russian | 1446 | 1234 |
| LMArena Spanish | 1464 | 1253 |
Instruction Following Kimi K2.6 leads
Kimi K2.6: 76.3 (#43), Llama 3.1-70B: 65.3 (#223)
| Benchmark | Kimi K2.6 | Llama 3.1-70B |
|---|---|---|
| LMArena Instruction Following | 1451 | 1231 |
| IFEval | — | 82.1% |
Long Context Kimi K2.6 leads
Kimi K2.6: 44.9 (#52), Llama 3.1-70B: 37.6 (#214)
| Benchmark | Kimi K2.6 | Llama 3.1-70B |
|---|---|---|
| LMArena Longer Query | 1468 | 1241 |
Writing & Preference Kimi K2.6 leads
Kimi K2.6: 68.5 (#26), Llama 3.1-70B: 35.4 (#267)
| Benchmark | Kimi K2.6 | Llama 3.1-70B |
|---|---|---|
| LMArena Text | 1455 | 1261 |
| LMArena Creative Writing | 1434 | 1232 |
| EQ-Bench Creative Writing | 1725 | 784 |
| LMArena Multi-Turn | 1453 | 1256 |
| WildBench | — | 75.8% |
| EQ-Bench 4 | 1202 | — |
Frequently asked questions
Is Kimi K2.6 better than Llama 3.1-70B?
Kimi K2.6 is the stronger model overall, scoring 47.7 to 29.6 on the Noometry Index. Llama 3.1-70B costs 4.3× less per token, which makes it the better buy when Kimi K2.6's lead doesn't matter for your workload.
Which is cheaper, Kimi K2.6 or Llama 3.1-70B?
Llama 3.1-70B is cheaper. It lists at $0.40 per million input tokens and $0.40 per million output tokens; Kimi K2.6 lists at $0.95 and $4.
Is Kimi K2.6 or Llama 3.1-70B better for coding?
Kimi K2.6 scores higher on coding benchmarks: 50.7 versus 30.3 in the Noometry coding category.
Which has the bigger context window?
Kimi K2.6 does, with 262K tokens against 128K.
How many benchmarks do Kimi K2.6 and Llama 3.1-70B share?
24 benchmarks have published results for both models. Kimi K2.6 has 51 scored results on Noometry and Llama 3.1-70B has 35.