Model comparison
Kimi K2.6 vs LFM2.5-VL-450M
Head-to-head evidence from 1 shared benchmark result across 1 category. Overall scores shown here use BenchLM's provisional ranking lane.
Verified leaderboard positions: Kimi K2.6 #13; LFM2.5-VL-450M unranked
Evidence parity. Kimi K2.6 and LFM2.5-VL-450M share 1 comparable benchmark result. 1 of 8 categories are comparable. 59 results are unique to Kimi K2.6; 6 to LFM2.5-VL-450M.
Updated July 13, 2026- Shared results
- 1
- Kimi K2.6 only
- 59
- LFM2.5-VL-450M only
- 6
- Comparable categories
- 1 / 8
Pick Kimi K2.6 if you want the stronger benchmark profile. LFM2.5-VL-450M only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Confidence note. This is a partial-evidence comparison with 1 shared benchmark result across 1 evidence category; 1 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Kimi K2.6 is clearly ahead on the provisional aggregate, 74 to 34. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Kimi K2.6's sharpest advantage is in knowledge, where it averages 42.2 against 20.5. The single biggest benchmark swing on the page is GPQA, 90.5% to 25.7%.
Kimi K2.6 is also the more expensive model on tokens at $0.95 input / $4.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for LFM2.5-VL-450M. That is roughly Infinityx on output cost alone. Kimi K2.6 is the reasoning model in the pair, while LFM2.5-VL-450M is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Kimi K2.6 gives you the larger context window at 256K, compared with 128K for LFM2.5-VL-450M.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | Kimi K2.6 | Δ | LFM2.5-VL-450M |
|---|---|---|---|
| Knowledge | Kimi K2.642.2 | Margin← 21.7 | LFM2.5-VL-450M20.5 |
| Agentic | Kimi K2.673.5 | MarginNo overlap | LFM2.5-VL-450MNot measured |
| Coding | Kimi K2.672.6 | MarginNo overlap | LFM2.5-VL-450MNot measured |
| Math | Kimi K2.667.1 | MarginNo overlap | LFM2.5-VL-450MNot measured |
| Multimodal | Kimi K2.679.8 | MarginNo overlap | LFM2.5-VL-450MNot measured |
| Inst. Following | Kimi K2.6Not measured | MarginNo overlap | LFM2.5-VL-450M61.2 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
GPQA
KnowledgeA 90.5%B 25.7%Winner: Kimi K2.6Δ 64.8GPQA: Kimi K2.6 scored 90.5%; LFM2.5-VL-450M scored 25.7%. Kimi K2.6 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Kimi K2.6 | LFM2.5-VL-450M | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Kimi K2.6$0.95 input / $4 output | LFM2.5-VL-450M$0 input / $0 output | LFM2.5-VL-450M has the lower combined listed price. |
| Generation speedtokens per second | Kimi K2.6Not available | LFM2.5-VL-450MNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Kimi K2.6Not available | LFM2.5-VL-450MNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Kimi K2.6256K | LFM2.5-VL-450M128K | Kimi K2.6 lists the larger context window. |
Benchmark Deep Dive
Agentic23 benchmarks
| Benchmark | Kimi K2.6 | LFM2.5-VL-450M | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 66.7% | — | Not comparable |
| BrowseCompSource | 83.2% | — | Not comparable |
| OSWorld-VerifiedSource | 73.1% | — | Not comparable |
| ToolathlonSource | 50% | — | Not comparable |
| MCP AtlasSource | 55.9% | — | Not comparable |
| Claw-EvalSource | 62.3% | — | Not comparable |
| DeepSearchQASource | 92.5% | — | Not comparable |
| WideResearchSource | 80.8% | — | Not comparable |
| AA Agentic IndexSource | 30.3% | — | Not comparable |
| Tau2-TelecomSource | 95.9% | — | Not comparable |
| GDPval-AASource | 34.5% | — | Not comparable |
| GDPval-AASource | 1190 | — | Not comparable |
| APEX-Agents-AASource | 28.5% | — | Not comparable |
| Gert LabsSource | 56.82% | — | Not comparable |
| ResearchClawBenchSource | 18.0% | — | Not comparable |
| OSWorld 2.0Source | 4.6% | — | Not comparable |
| AA BriefcaseSource | 809 | — | Not comparable |
| AA AutomationBenchSource | 19.6% | — | Not comparable |
| AA EnterpriseOps-GymSource | 38.5% | — | Not comparable |
| AA Harvey LABSource | 0.0% | — | Not comparable |
| AA ITBenchSource | 31.2% | — | Not comparable |
| AA Tau3 BankingSource | 20.6% | — | Not comparable |
| BFCL v4Source | — | 21.1% | Not comparable |
Coding13 benchmarks
| Benchmark | Kimi K2.6 | LFM2.5-VL-450M | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 80.2% | — | Not comparable |
| LiveCodeBenchSource | 89.6% | — | Not comparable |
| LiveCodeBench v6Source | 89.6% | — | Not comparable |
| SWE-bench ProSource | 58.6% | — | Not comparable |
| SWE MultilingualSource | 76.7% | — | Not comparable |
| SciCodeSource | 52.2% | — | Not comparable |
| Terminal-Bench 2.0Source | 66.7% | — | Not comparable |
| Vibe Code BenchSource | 37.89% | — | Not comparable |
| cursorBench31Source | 47.6% | — | Not comparable |
| AA Coding IndexSource | 61.8% | — | Not comparable |
| Terminal-Bench HardSource | 43.9% | — | Not comparable |
| AA-SciCodeSource | 53.5% | — | Not comparable |
| AA Terminal-Bench 2.1Source | 65.9% | — | Not comparable |
Reasoning2 benchmarks
KnowledgeKimi K2.6 wins11 benchmarks
| Benchmark | Kimi K2.6 | LFM2.5-VL-450M | Result |
|---|---|---|---|
| GPQASource | 90.5% | 25.7% | Kimi K2.6 leads |
| GPQA-DSource | 90.5% | — | Not comparable |
| HLESource | 34.7% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 44.2% | — | Not comparable |
| AA-GPQA DiamondSource | 91.1% | — | Not comparable |
| AA-HLESource | 35.9% | — | Not comparable |
| AA-Omniscience IndexSource | 6.4% | — | Not comparable |
| AA-Omniscience AccuracySource | 32.8% | — | Not comparable |
| AA-Omniscience Hallucination RateSource | 39.3% | — | Not comparable |
| AA Openness IndexSource | 33.3% | — | Not comparable |
| MMLU-ProSource | — | 19.3% | Not comparable |
Math5 benchmarks
Multimodal10 benchmarks
| Benchmark | Kimi K2.6 | LFM2.5-VL-450M | Result |
|---|---|---|---|
| MMMU-ProSource | 79.4% | — | Not comparable |
| MMMU-Pro w/ PythonSource | 80.1% | — | Not comparable |
| CharXivSource | 80.4% | — | Not comparable |
| MathVisionSource | 87.4% | — | Not comparable |
| V*Source | 96.9% | — | Not comparable |
| AA-MMMU-ProSource | 79.4% | — | Not comparable |
| Design Arena WebsiteSource | 1318 | — | Not comparable |
| MMMUSource | — | 32.7% | Not comparable |
| RealWorldQASource | — | 58.4% | Not comparable |
| CountBenchSource | — | 73.3% | Not comparable |
Frequently Asked Questions (2)
Which is better, Kimi K2.6 or LFM2.5-VL-450M?
Kimi K2.6 is ahead on BenchLM's provisional leaderboard, 74 to 34. The biggest single separator in this matchup is GPQA, where the scores are 90.5% and 25.7%.
Which is better for knowledge tasks, Kimi K2.6 or LFM2.5-VL-450M?
Kimi K2.6 has the edge for knowledge tasks in this comparison, averaging 42.2 versus 20.5. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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