Model comparison
Gemma 2 27B vs Kimi K2 (Jul 2025)
Kimi K2 (Jul 2025) is the stronger model overall, scoring 40.9 to 29.2 on the Noometry Index. Gemma 2 27B costs 1.5× less per token, which makes it the better buy when Kimi K2 (Jul 2025)'s lead doesn't matter for your workload.
Last verified . 19 shared benchmarks.
GoogleSummary
- They share 19 benchmarks with published results for both. Gemma 2 27B scores higher in 0 categories and Kimi K2 (Jul 2025) in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Kimi K2 (Jul 2025) leads 40.1 to 10.0.
- The biggest single-benchmark swing is Confabulations: 27.1% for Gemma 2 27B and 20.4% for Kimi K2 (Jul 2025).
- Gemma 2 27B is cheaper at $0.65 / $0.65 per million input/output tokens, against $0.57 / $2.30 for Kimi K2 (Jul 2025).
- Kimi K2 (Jul 2025) accepts more context: 262K tokens versus 8K.
Side by side
| Gemma 2 27B | Kimi K2 (Jul 2025) | |
|---|---|---|
| Provider | Moonshot AI | |
| Noometry Index | 29.2 | 40.9 |
| Released | 2024-06-24 | 2025-07-12 |
| Weights | Open | Open |
| Context window | 8K | 262K |
| Max output | 2K | 262K |
| Input $ / M tokens | $0.65 | $0.57 |
| Output $ / M tokens | $0.65 | $2.30 |
| Results tracked | 34 | 42 |
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Category by category
Coding Kimi K2 (Jul 2025) leads
Gemma 2 27B: 33.5 (#243), Kimi K2 (Jul 2025): 43.1 (#83)
| Benchmark | Gemma 2 27B | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Coding | 1211 | 1399 |
| SWE-bench Verified (bash only) | — | 63.4% |
| Aider Polyglot | — | 59.1% |
| GSO | — | 4.9% |
| WeirdML | — | 42.8% |
| BigCodeBench Instruct | 42.8% | — |
| LiveBench Coding | 36% | — |
| BigCodeBench Complete | 52.5% | — |
| ALE-Bench | — | 597.5 |
Agentic & Tool Use Not comparable
Gemma 2 27B: —, Kimi K2 (Jul 2025): 32.2 (#59)
| Benchmark | Gemma 2 27B | Kimi K2 (Jul 2025) |
|---|---|---|
| Terminal-Bench | — | 35.7% |
| Berkeley Function Calling Leaderboard | — | 59.1% |
| METR Time Horizons | — | 59.2% |
Reasoning Kimi K2 (Jul 2025) leads
Gemma 2 27B: 14.8 (#313), Kimi K2 (Jul 2025): 22.7 (#173)
| Benchmark | Gemma 2 27B | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Hard Prompts | 1199 | 1384 |
| Epoch Capabilities Index | 122.06 | 146.02 |
| SimpleBench | — | 26.3% |
| Kagi LLM Benchmark | — | 64.4% |
| LiveBench Reasoning | 28.1% | — |
| DTBench | 48% | — |
| LiveBench Data Analysis | 47.9% | — |
| LMCA | 7.1% | — |
| ForecastBench | — | 60.2 |
| LiveBench | 38.2% | — |
Math Kimi K2 (Jul 2025) leads
Gemma 2 27B: 10.0 (#306), Kimi K2 (Jul 2025): 40.1 (#81)
| Benchmark | Gemma 2 27B | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Math | 1212 | 1396 |
| OTIS Mock AIME 2024-2025 | 1.4% | — |
| Omni-MATH | — | 65.4% |
| LiveBench Math | 26.5% | — |
| MATH Level 5 | 27.9% | — |
| FrontierMath (Feb 2025 set) | — | 21.4% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Kimi K2 (Jul 2025) leads
Gemma 2 27B: 18.9 (#275), Kimi K2 (Jul 2025): 36.9 (#153)
| Benchmark | Gemma 2 27B | Kimi K2 (Jul 2025) |
|---|---|---|
| Confabulations | 27.1% | 20.4% |
| LMArena Expert | 1172 | 1363 |
| GPQA Diamond | 36.5% | — |
| MMLU-Pro | — | 81.9% |
| Vectara Hallucination Rate | — | 17.9% |
| GPQA (HELM) | — | 65.3% |
| MMLU | 75.7% | — |
Multilingual Kimi K2 (Jul 2025) leads
Gemma 2 27B: 38.8 (#221), Kimi K2 (Jul 2025): 49.8 (#125)
| Benchmark | Gemma 2 27B | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Non-English | 1217 | 1372 |
| LMArena Chinese | 1221 | 1416 |
| LMArena French | 1246 | 1379 |
| LMArena German | 1209 | 1386 |
| LMArena Japanese | 1175 | 1349 |
| LMArena Korean | 1174 | 1324 |
| LMArena Russian | 1234 | 1384 |
| LMArena Spanish | 1228 | 1383 |
Instruction Following Kimi K2 (Jul 2025) leads
Gemma 2 27B: 60.7 (#244), Kimi K2 (Jul 2025): 71.3 (#151)
| Benchmark | Gemma 2 27B | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Instruction Following | 1206 | 1347 |
| LiveBench Instruction Following | 58.1% | — |
| IFEval | — | 85% |
Long Context Kimi K2 (Jul 2025) leads
Gemma 2 27B: 37.3 (#213), Kimi K2 (Jul 2025): 41.2 (#140)
| Benchmark | Gemma 2 27B | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Longer Query | 1231 | 1353 |
| Fiction.LiveBench | — | 66.7% |
| CL-bench | — | 17.6% |
Writing & Preference Kimi K2 (Jul 2025) leads
Gemma 2 27B: 44.4 (#220), Kimi K2 (Jul 2025): 62.5 (#76)
| Benchmark | Gemma 2 27B | Kimi K2 (Jul 2025) |
|---|---|---|
| LMArena Text | 1231 | 1380 |
| LMArena Creative Writing | 1241 | 1349 |
| LMArena Multi-Turn | 1224 | 1371 |
| Short-Story Creative Writing | — | 85.6% |
| EQ-Bench Creative Writing | — | 1666 |
| WildBench | — | 86.2% |
| LiveBench Language | 32.6% | — |
Frequently asked questions
Is Gemma 2 27B better than Kimi K2 (Jul 2025)?
Kimi K2 (Jul 2025) is the stronger model overall, scoring 40.9 to 29.2 on the Noometry Index. Gemma 2 27B costs 1.5× less per token, which makes it the better buy when Kimi K2 (Jul 2025)'s lead doesn't matter for your workload.
Which is cheaper, Gemma 2 27B or Kimi K2 (Jul 2025)?
Gemma 2 27B is cheaper. It lists at $0.65 per million input tokens and $0.65 per million output tokens; Kimi K2 (Jul 2025) lists at $0.57 and $2.30.
Is Gemma 2 27B or Kimi K2 (Jul 2025) better for coding?
Kimi K2 (Jul 2025) scores higher on coding benchmarks: 43.1 versus 33.5 in the Noometry coding category.
Which has the bigger context window?
Kimi K2 (Jul 2025) does, with 262K tokens against 8K.
How many benchmarks do Gemma 2 27B and Kimi K2 (Jul 2025) share?
19 benchmarks have published results for both models. Gemma 2 27B has 34 scored results on Noometry and Kimi K2 (Jul 2025) has 42.