Llama 4 Scout 17B 16E Instruct vs Kimi K2.6
At a Glance
| Compare |  Kimi K2.6Moonshot AI | |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | UnrankedNot in the 46-model eligible cohort | #32 of 4650.5 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 33.6–67.0 |
| CostLower is better · Published-token output estimate | UnrankedNot in the 44-model eligible cohort | #21 of 44$0.095 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | UnrankedNot in the 38-model eligible cohort | #24 of 3850.1 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 41.7–58.4 |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.10Deepinfra ↗ · Sep 21, 2026 | $0.75Deepinfra ↗ · Sep 21, 2026 |
| Output priceFrom · USD / 1M tokens | $0.30Deepinfra ↗ · Sep 21, 2026 | $3.50Deepinfra ↗ · Sep 21, 2026 |
| Context windowMaximum documented tokens | 10,000K | 262K |
| Model facts checked | Aug 28, 2026View model evidence → | Aug 28, 2026View model evidence → |
Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →
Available Benchmarks
| Benchmark | Llama-4-Scout-17B-16E-Instruct | Kimi-K2.6 |
|---|---|---|
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,279.2988% of row best · rating · llama-4-scout-17b-16e-instruct; 95% CI [1274.57681953, 1284.00026285]; votes 29740; rank 259 | 1,454.92100% of row best · rating · kimi-k2.6; 95% CI [1450.40277919, 1459.43693683]; votes 37502; rank 38 |
| LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · leader | 1,117.8087% of row best · rating · llama-4-scout-17b-16e-instruct; 95% CI [1108.35603674, 1127.24229078]; votes 6466; rank 114 | 1,280.40100% of row best · rating · kimi-k2.6; 95% CI [1273.38025179, 1287.42397051]; votes 15347; rank 28 |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 47.3258% of row best · points · Llama 4 Scout · 696 output tokens / case | 82.24100% of row best · points · Kimi K2.6 (thinking) · 18,080 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark winsNo overall winner | 2 benchmark winsNo overall winner |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | Llama-4-Scout-17B-16E-Instruct | Kimi-K2.6 |
|---|---|---|
| Developer | Meta | Moonshot AI |
| Family | Llama 4 Scout 17b 16e Instruct | Kimi K2 6 |
| Model | Llama-4-Scout-17B-16E-Instruct | Kimi-K2.6 |
| Version | Llama-4-Scout-17B-16E-Instruct | Kimi-K2.6 |
| Lifecycle | active | active |
| Released | 2025-04-05 | 2026-04-20 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 10,000K | 262K |
| Total parameters | 108.6B | 1T |
| Active parameters | 17B | 32B |
| License | other | other |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation, tools | chat, generation, reasoning, tools |
Llama 4 Scout 17B 16E Instruct Capabilities
Kimi K2.6 Capabilities
Related Comparisons
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| vs |  Mistral Large 3Mistral AI | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Llama 4 Scout 17B 16E Instruct vs Kimi K2.6 FAQs
Is Llama 4 Scout 17B 16E Instruct or Kimi K2.6 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 4 Scout 17B 16E Instruct and Kimi K2.6, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Llama 4 Scout 17B 16E Instruct or Kimi K2.6?+
Llama 4 Scout 17B 16E Instruct is $0.10 and Kimi K2.6 is $0.75 per million tokens, so Llama 4 Scout 17B 16E Instruct is cheaper on this metric. Llama 4 Scout 17B 16E Instruct is $0.30 and Kimi K2.6 is $3.50 per million tokens, so Llama 4 Scout 17B 16E Instruct is cheaper on this metric.
Which has a larger context window, Llama 4 Scout 17B 16E Instruct or Kimi K2.6?+
Llama 4 Scout 17B 16E Instruct has the larger sourced context window. Llama 4 Scout 17B 16E Instruct supports 10,000K and Kimi K2.6 supports 262K.
Which performs better in benchmarks, Llama 4 Scout 17B 16E Instruct or Kimi K2.6?+
There is no overall benchmark winner: An overall winner requires at least two decisive benchmarks from at least two original publishers.
Can Llama 4 Scout 17B 16E Instruct or Kimi K2.6 be self-hosted?+
Both models have the same recorded self-hosting status: supported. Llama 4 Scout 17B 16E Instruct is open weight; Kimi K2.6 is open weight.
Can Llama 4 Scout 17B 16E Instruct and Kimi K2.6 understand images?+
Llama 4 Scout 17B 16E Instruct is documented with image input; Kimi K2.6 is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Llama 4 Scout 17B 16E Instruct or Kimi K2.6?+
Neither has a larger sourced maximum output. Llama 4 Scout 17B 16E Instruct is — and Kimi K2.6 is —.
Do Llama 4 Scout 17B 16E Instruct and Kimi K2.6 support reasoning and tool use?+
Llama 4 Scout 17B 16E Instruct: tool calling and image input. Kimi K2.6: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Llama 4 Scout 17B 16E Instruct or Kimi K2.6?+
Llama 4 Scout 17B 16E Instruct has 4 sourced provider routes; Kimi K2.6 has 5, so Kimi K2.6 has broader tracked availability.
Which offers better value, Llama 4 Scout 17B 16E Instruct or Kimi K2.6?+
There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.