Model comparison
Kimi K2.5 vs Llama-3.3-70B-Instruct
Kimi K2.5 is the stronger model overall, scoring 48.1 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 5.8× less per token, which makes it the better buy when Kimi K2.5's lead doesn't matter for your workload.
Last verified . 26 shared benchmarks.
MetaSummary
- They share 26 benchmarks with published results for both. Kimi K2.5 scores higher in 9 categories and Llama-3.3-70B-Instruct in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where Kimi K2.5 leads 51.8 to 15.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 92.2% for Kimi K2.5 and 5.1% for Llama-3.3-70B-Instruct.
- Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 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-3.3-70B-Instruct | |
|---|---|---|
| Provider | Moonshot AI | Meta |
| Noometry Index | 48.1 | 30.6 |
| Released | 2026-01-27 | 2024-12-06 |
| Weights | Open | Open |
| Context window | 262K | 128K |
| Max output | 262K | 4K |
| Input $ / M tokens | $0.45 | $0.10 |
| Output $ / M tokens | $2.25 | $0.32 |
| Results tracked | 51 | 43 |
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Category by category
Coding Kimi K2.5 leads
Kimi K2.5: 48.8 (#53), Llama-3.3-70B-Instruct: 31.0 (#290)
| Benchmark | Kimi K2.5 | Llama-3.3-70B-Instruct |
|---|---|---|
| SciCode | 49% | 26% |
| WeirdML | 45.6% | 14.4% |
| LMArena Coding | 1474 | 1268 |
| SWE-bench Verified | 73.8% | — |
| SWE-bench Verified (bash only) | 70.8% | — |
| LMArena WebDev | 1437 | — |
| SWE-bench Multilingual | 67.3% | — |
| BigCodeBench Instruct | — | 46.9% |
| LiveBench Coding | — | 36.6% |
| BigCodeBench Complete | — | 57.5% |
| ALE-Bench | 821.65 | — |
Agentic & Tool Use Kimi K2.5 leads
Kimi K2.5: 34.2 (#48), Llama-3.3-70B-Instruct: 25.8 (#105)
| Benchmark | Kimi K2.5 | Llama-3.3-70B-Instruct |
|---|---|---|
| Terminal-Bench | 43.2% | — |
| Berkeley Function Calling Leaderboard | — | 31.9% |
| OSWorld | 63.3% | — |
| BALROG | — | 23% |
| Vending-Bench 2 | 1,198 | — |
Reasoning Kimi K2.5 leads
Kimi K2.5: 31.2 (#80), Llama-3.3-70B-Instruct: 14.1 (#327)
| Benchmark | Kimi K2.5 | Llama-3.3-70B-Instruct |
|---|---|---|
| SimpleBench | 46.8% | 19.9% |
| CritPt | 3.1% | 0% |
| LMArena Hard Prompts | 1453 | 1257 |
| Epoch Capabilities Index | 148.03 | 127.33 |
| ARC-AGI-2 | 11.8% | — |
| Kagi LLM Benchmark | 78.5% | — |
| NYT Connections (extended) | 69.9% | — |
| ARC-AGI-1 | 65.3% | — |
| Chess Puzzles | 12% | — |
| EnigmaEval | 3.4% | — |
| Thematic Generalization | 69.4% | — |
| LiveBench Reasoning | — | 50.8% |
| DTBench | — | 59.5% |
| LiveBench Data Analysis | — | 49.5% |
| LMCA | — | 17.5% |
| ForecastBench | — | 58.6 |
| LiveBench | — | 50.2% |
Math Kimi K2.5 leads
Kimi K2.5: 51.8 (#53), Llama-3.3-70B-Instruct: 15.3 (#298)
| Benchmark | Kimi K2.5 | Llama-3.3-70B-Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 92.2% | 5.1% |
| LMArena Math | 1470 | 1267 |
| MathArena Final-Answer Competitions | 62.3% | — |
| LiveBench Math | — | 42.2% |
| MATH Level 5 | — | 41.6% |
| FrontierMath (Feb 2025 set) | 27.9% | — |
| FrontierMath Tier 4 (v1) | 4.2% | — |
Knowledge Kimi K2.5 leads
Kimi K2.5: 53.6 (#56), Llama-3.3-70B-Instruct: 30.6 (#226)
| Benchmark | Kimi K2.5 | Llama-3.3-70B-Instruct |
|---|---|---|
| GPQA Diamond | 87.6% | 47.4% |
| Vectara Hallucination Rate | 14.2% | 4.1% |
| LMArena Expert | 1466 | 1225 |
| Humanity's Last Exam | 24.4% | — |
| SimpleQA Verified | 34.3% | — |
| Confabulations | — | 22.8% |
| MMLU | — | 86.3% |
Multimodal Not comparable
Kimi K2.5: 41.1 (#39), Llama-3.3-70B-Instruct: —
| Benchmark | Kimi K2.5 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Vision | 1269 | — |
| LMArena Document | 1430 | — |
Multilingual Kimi K2.5 leads
Kimi K2.5: 53.9 (#53), Llama-3.3-70B-Instruct: 39.9 (#220)
| Benchmark | Kimi K2.5 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Non-English | 1433 | 1236 |
| LMArena Chinese | 1495 | 1217 |
| LMArena French | 1454 | 1281 |
| LMArena German | 1441 | 1251 |
| LMArena Japanese | 1421 | 1150 |
| LMArena Korean | 1410 | 1143 |
| LMArena Russian | 1435 | 1252 |
| LMArena Spanish | 1450 | 1270 |
Instruction Following Kimi K2.5 leads
Kimi K2.5: 75.3 (#64), Llama-3.3-70B-Instruct: 71.1 (#157)
| Benchmark | Kimi K2.5 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Instruction Following | 1431 | 1242 |
| LiveBench Instruction Following | — | 82.7% |
Long Context Kimi K2.5 leads
Kimi K2.5: 52.1 (#7), Llama-3.3-70B-Instruct: 26.4 (#295)
| Benchmark | Kimi K2.5 | Llama-3.3-70B-Instruct |
|---|---|---|
| Fiction.LiveBench | 86.1% | 33.3% |
| LMArena Longer Query | 1445 | 1256 |
| CL-bench | 19.3% | — |
| CL-bench Life | 13.2% | — |
Writing & Preference Kimi K2.5 leads
Kimi K2.5: 65.1 (#53), Llama-3.3-70B-Instruct: 47.6 (#207)
| Benchmark | Kimi K2.5 | Llama-3.3-70B-Instruct |
|---|---|---|
| LMArena Text | 1445 | 1274 |
| LMArena Creative Writing | 1423 | 1250 |
| LMArena Multi-Turn | 1444 | 1280 |
| EQ-Bench Creative Writing | 1579 | — |
| LiveBench Language | — | 39.2% |
Frequently asked questions
Is Kimi K2.5 better than Llama-3.3-70B-Instruct?
Kimi K2.5 is the stronger model overall, scoring 48.1 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 5.8× 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-3.3-70B-Instruct?
Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Kimi K2.5 lists at $0.45 and $2.25.
Is Kimi K2.5 or Llama-3.3-70B-Instruct better for coding?
Kimi K2.5 scores higher on coding benchmarks: 48.8 versus 31.0 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-3.3-70B-Instruct share?
26 benchmarks have published results for both models. Kimi K2.5 has 51 scored results on Noometry and Llama-3.3-70B-Instruct has 43.