Model comparison
Kimi K2 vs LFM2.5-1.2B-Thinking
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Kimi K2 #199 (Supported); LFM2.5-1.2B-Thinking #209 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Kimi K2 and LFM2.5-1.2B-Thinking share 0 comparable benchmark results. 0 of 8 categories are comparable. 14 results are unique to Kimi K2; 0 to LFM2.5-1.2B-Thinking.
Updated July 28, 2026- Shared results
- 0
- Kimi K2 only
- 14
- LFM2.5-1.2B-Thinking only
- 0
- Comparable categories
- 0 / 8
Benchmark data for Kimi K2 and LFM2.5-1.2B-Thinking is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM does not have sourced benchmark coverage for LFM2.5-1.2B-Thinking yet. This comparison is currently limited to metadata such as context window, reasoning mode, and pricing where available.
Kimi K2 is priced at $0.60 input / $2.50 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for LFM2.5-1.2B-Thinking. Kimi K2 has the larger context window at 128K, compared with 32K for LFM2.5-1.2B-Thinking.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Kimi K2 | LFM2.5-1.2B-Thinking | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Kimi K2$0.6 input / $2.5 output | LFM2.5-1.2B-Thinking$0 input / $0 output | LFM2.5-1.2B-Thinking has the lower combined listed price. |
| Generation speedtokens per second | Kimi K243 tok/s | LFM2.5-1.2B-ThinkingNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Kimi K21.51 s | LFM2.5-1.2B-ThinkingNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Kimi K2128K | LFM2.5-1.2B-Thinking32K | Kimi K2 lists the larger context window. |
Benchmark Deep Dive
Agentic1 benchmarks
| Benchmark | Kimi K2 | LFM2.5-1.2B-Thinking | Result |
|---|---|---|---|
| τ²-bench resultsSource | 61.1% | — | Not comparable |
Coding1 benchmarks
| Benchmark | Kimi K2 | LFM2.5-1.2B-Thinking | Result |
|---|---|---|---|
| AA-SciCodeSource | 34.5% | — | Not comparable |
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | Kimi K2 | LFM2.5-1.2B-Thinking | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 19.4% | — | Not comparable |
| AA-GPQA DiamondSource | 76.6% | — | Not comparable |
| AA-HLESource | 7.0% | — | Not comparable |
| AA-Omniscience IndexSource | -27.5% | — | Not comparable |
| AA-Omniscience AccuracySource | 26.8% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 74.2% | — | Not comparable |
Math2 benchmarks
Multimodal1 benchmarks
| Benchmark | Kimi K2 | LFM2.5-1.2B-Thinking | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1075 | — | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Kimi K2 | LFM2.5-1.2B-Thinking | Result |
|---|---|---|---|
| AA-IFBenchSource | 41.5% | — | Not comparable |
Frequently Asked Questions (3)
Can I compare Kimi K2 and LFM2.5-1.2B-Thinking on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “coming soon”?
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
What data is available for Kimi K2 and LFM2.5-1.2B-Thinking today?
Kimi K2: $0.60 input / $2.50 output per 1M tokens LFM2.5-1.2B-Thinking: $0.00 input / $0.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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