Model comparison
Kimi K2.5 vs Llama 3.1-70B
Kimi K2.5 is the stronger model overall, scoring 47.8 to 29.4 on the Noometry Index. Llama 3.1-70B costs 2.3× less per token, which makes it the better buy when Kimi K2.5's lead doesn't matter for your workload.
Last verified . 22 shared benchmarks.
MetaSummary
- They share 22 benchmarks with published results for both. Kimi K2.5 scores higher in 9 categories and Llama 3.1-70B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Kimi K2.5 leads 50.3 to 12.7.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 92.2% for Kimi K2.5 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 $0.45 / $2.25 for Kimi K2.5.
- Kimi K2.5 accepts more context: 262K tokens versus 128K.
Side by side
| Kimi K2.5 | Llama 3.1-70B | |
|---|---|---|
| Provider | Moonshot AI | Meta |
| Noometry Index | 47.8 | 29.4 |
| Released | 2026-01-27 | 2024-07-23 |
| Weights | Open | Open |
| Context window | 262K | 128K |
| Max output | 262K | 4K |
| Input $ / M tokens | $0.45 | $0.40 |
| Output $ / M tokens | $2.25 | $0.40 |
| Results tracked | 51 | 35 |
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Category by category
Coding Kimi K2.5 leads
Kimi K2.5: 48.7 (#48), Llama 3.1-70B: 29.8 (#292)
| Benchmark | Kimi K2.5 | Llama 3.1-70B |
|---|---|---|
| WeirdML | 45.6% | 9% |
| LMArena Coding | 1474 | 1260 |
| SWE-bench Verified | 73.8% | — |
| SWE-bench Verified (bash only) | 70.8% | — |
| LMArena WebDev | 1436 | — |
| SWE-bench Multilingual | 67.3% | — |
| SciCode | 49% | — |
| BigCodeBench Instruct | — | 46.1% |
| BigCodeBench Complete | — | 54.8% |
| ALE-Bench | 821.65 | — |
Agentic & Tool Use Kimi K2.5 leads
Kimi K2.5: 34.0 (#44), Llama 3.1-70B: 24.9 (#104)
| Benchmark | Kimi K2.5 | Llama 3.1-70B |
|---|---|---|
| Terminal-Bench | 43.2% | — |
| TheAgentCompany | — | 6.9% |
| OSWorld | 63.3% | — |
| BALROG | — | 27.9% |
| Vending-Bench 2 | 1,198 | — |
Reasoning Kimi K2.5 leads
Kimi K2.5: 31.2 (#74), Llama 3.1-70B: 20.8 (#215)
| Benchmark | Kimi K2.5 | Llama 3.1-70B |
|---|---|---|
| LMArena Hard Prompts | 1454 | 1241 |
| Epoch Capabilities Index | 148.05 | 125.91 |
| ARC-AGI-2 | 11.8% | — |
| SimpleBench | 46.8% | — |
| Kagi LLM Benchmark | 78.5% | — |
| NYT Connections (extended) | 69.9% | — |
| ARC-AGI-1 | 65.3% | — |
| CritPt | 3.1% | — |
| Chess Puzzles | 12% | — |
| EnigmaEval | 3.4% | — |
| Thematic Generalization | 69.4% | — |
| DTBench | — | 60% |
| LMCA | — | 14.8% |
Math Kimi K2.5 leads
Kimi K2.5: 50.3 (#46), Llama 3.1-70B: 12.7 (#298)
| Benchmark | Kimi K2.5 | Llama 3.1-70B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 92.2% | 3.6% |
| LMArena Math | 1470 | 1252 |
| MathArena Final-Answer Competitions | 62.3% | — |
| Omni-MATH | — | 21% |
| MATH Level 5 | — | 36.7% |
| FrontierMath (Feb 2025 set) | 27.9% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge Kimi K2.5 leads
Kimi K2.5: 53.6 (#52), Llama 3.1-70B: 24.1 (#264)
| Benchmark | Kimi K2.5 | Llama 3.1-70B |
|---|---|---|
| GPQA Diamond | 87.6% | 44.2% |
| LMArena Expert | 1466 | 1209 |
| Humanity's Last Exam | 24.4% | — |
| SimpleQA Verified | 34.3% | — |
| MMLU-Pro | — | 65.3% |
| Vectara Hallucination Rate | 14.2% | — |
| GPQA (HELM) | — | 42.6% |
| MMLU | — | 80.1% |
Multimodal Not comparable
Kimi K2.5: 40.4 (#37), Llama 3.1-70B: —
| Benchmark | Kimi K2.5 | Llama 3.1-70B |
|---|---|---|
| LMArena Vision | 1269 | — |
| LMArena Document | 1430 | — |
Multilingual Kimi K2.5 leads
Kimi K2.5: 54.1 (#50), Llama 3.1-70B: 38.9 (#220)
| Benchmark | Kimi K2.5 | Llama 3.1-70B |
|---|---|---|
| LMArena Non-English | 1433 | 1219 |
| LMArena Chinese | 1495 | 1215 |
| LMArena French | 1454 | 1260 |
| LMArena German | 1441 | 1222 |
| LMArena Japanese | 1422 | 1132 |
| LMArena Korean | 1412 | 1140 |
| LMArena Russian | 1436 | 1234 |
| LMArena Spanish | 1450 | 1253 |
Instruction Following Kimi K2.5 leads
Kimi K2.5: 75.5 (#60), Llama 3.1-70B: 65.5 (#218)
| Benchmark | Kimi K2.5 | Llama 3.1-70B |
|---|---|---|
| LMArena Instruction Following | 1431 | 1231 |
| IFEval | — | 82.1% |
Long Context Kimi K2.5 leads
Kimi K2.5: 52.1 (#7), Llama 3.1-70B: 37.6 (#208)
| Benchmark | Kimi K2.5 | Llama 3.1-70B |
|---|---|---|
| LMArena Longer Query | 1445 | 1241 |
| Fiction.LiveBench | 86.1% | — |
| CL-bench | 19.3% | — |
| CL-bench Life | 13.2% | — |
Writing & Preference Kimi K2.5 leads
Kimi K2.5: 65.3 (#51), Llama 3.1-70B: 35.6 (#262)
| Benchmark | Kimi K2.5 | Llama 3.1-70B |
|---|---|---|
| LMArena Text | 1445 | 1261 |
| LMArena Creative Writing | 1423 | 1232 |
| EQ-Bench Creative Writing | 1579 | 784 |
| LMArena Multi-Turn | 1444 | 1256 |
| WildBench | — | 75.8% |
Frequently asked questions
Is Kimi K2.5 better than Llama 3.1-70B?
Kimi K2.5 is the stronger model overall, scoring 47.8 to 29.4 on the Noometry Index. Llama 3.1-70B costs 2.3× less per token, which makes it the better buy when Kimi K2.5's lead doesn't matter for your workload.
Which is cheaper, Kimi K2.5 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 K2.5 lists at $0.45 and $2.25.
Is Kimi K2.5 or Llama 3.1-70B better for coding?
Kimi K2.5 scores higher on coding benchmarks: 48.7 versus 29.8 in the Noometry coding category.
Which has the bigger context window?
Kimi K2.5 does, with 262K tokens against 128K.
How many benchmarks do Kimi K2.5 and Llama 3.1-70B share?
22 benchmarks have published results for both models. Kimi K2.5 has 51 scored results on Noometry and Llama 3.1-70B has 35.