Model comparison
Kimi K2 vs Llama 4 Scout
Head-to-head evidence from 13 shared benchmark results across 7 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Kimi K2 #189 (Supported); Llama 4 Scout #174 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Kimi K2 and Llama 4 Scout share 13 comparable benchmark results. 0 of 8 categories are comparable. 1 result is unique to Kimi K2; 5 to Llama 4 Scout.
Updated July 20, 2026- Shared results
- 13
- Kimi K2 only
- 1
- Llama 4 Scout only
- 5
- Comparable categories
- 0 / 8
Benchmark data for Kimi K2 and Llama 4 Scout is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 13 shared benchmark results across 7 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 has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
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 Llama 4 Scout. Llama 4 Scout has the larger context window at 10M, compared with 128K for Kimi K2.
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 | Δ | Llama 4 Scout |
|---|---|---|---|
| Math | Kimi K216.1 | MarginNo overlap | Llama 4 ScoutNot measured |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
FrontierMath v2 (Tiers 1-3)
MathA 21.404%B 0.000%Winner: Kimi K2Δ 21.4FrontierMath v2 (Tiers 1-3): Kimi K2 scored 21.404%; Llama 4 Scout scored 0.000%. Kimi K2 wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Kimi K2 | Llama 4 Scout | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Kimi K2$0.6 input / $2.5 output | Llama 4 Scout$0 input / $0 output | Llama 4 Scout has the lower combined listed price. |
| Generation speedtokens per second | Kimi K243 tok/s | Llama 4 Scout128 tok/s | Llama 4 Scout has the higher measured throughput. |
| First-answer latencyseconds to first token | Kimi K21.51 s | Llama 4 Scout0.70 s | Llama 4 Scout reaches the first token sooner. |
| Context windowmaximum listed tokens | Kimi K2128K | Llama 4 Scout10M | Llama 4 Scout lists the larger context window. |
Benchmark Deep Dive
Agentic4 benchmarks
Coding2 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | Kimi K2 | Llama 4 Scout | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 19.4% | 10.0% | Kimi K2 leads |
| AA-GPQA DiamondSource | 76.6% | 58.7% | Kimi K2 leads |
| AA-HLESource | 7.0% | 4.3% | Kimi K2 leads |
| AA-Omniscience IndexSource | -27.5% | -52.4% | Kimi K2 leads |
| AA-Omniscience AccuracySource | 26.8% | 14.6% | Kimi K2 leads |
| AA-Omniscience Hallucination RateSource | 74.2% | 78.3% | Kimi K2 leads |
Math2 benchmarks
Multimodal2 benchmarks
Inst. Following1 benchmarks
| Benchmark | Kimi K2 | Llama 4 Scout | Result |
|---|---|---|---|
| AA-IFBenchSource | 41.5% | 39.5% | Kimi K2 leads |
Frequently Asked Questions (3)
Can I compare Kimi K2 and Llama 4 Scout 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 Llama 4 Scout today?
Kimi K2: $0.60 input / $2.50 output per 1M tokens Llama 4 Scout: $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.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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