Llama 4 Scout 17B 16E Instruct vs Kimi K2.5
At a Glance
| Compare |  Kimi K2.5Moonshot AI | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.10Deepinfra ↗ · Sep 21, 2026 | $0.45Deepinfra ↗ · Aug 29, 2026 |
| Output priceFrom · USD / 1M tokens | $0.30Deepinfra ↗ · Sep 21, 2026 | $2.25Deepinfra ↗ · Aug 29, 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-K2.5 |
|---|---|---|
| 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 | 1,445.64100% of row best · rating · kimi-k2.5-thinking; 95% CI [1442.30343195, 1448.97544647]; votes 70513; rank 53 |
| LMArena Vision Arenavision-2026-09-13-d25aabda0010 · arena_rating · leader | 1,117.8088% of row best · rating · llama-4-scout-17b-16e-instruct; 95% CI [1108.35603674, 1127.24229078]; votes 6466; rank 114 | 1,266.99100% of row best · rating · kimi-k2.5-thinking; 95% CI [1261.13170482, 1272.84453452]; votes 26646; rank 39 |
| 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-K2.5 |
|---|---|---|
| Developer | Meta | Moonshot AI |
| Family | Llama 4 Scout 17b 16e Instruct | Kimi K2 5 |
| Model | Llama-4-Scout-17B-16E-Instruct | Kimi-K2.5 |
| Version | Llama-4-Scout-17B-16E-Instruct | Kimi-K2.5 |
| Lifecycle | active | active |
| Released | 2025-04-05 | 2026-01-27 |
| 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), Hugging Face (Standard), Openrouter (Standard) |
| Capabilities | chat, generation, tools | chat, generation, reasoning, tools |
Llama 4 Scout 17B 16E Instruct Capabilities
Kimi K2.5 Capabilities
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Primary Evidence
Sources and Freshness
Questions
Llama 4 Scout 17B 16E Instruct vs Kimi K2.5 FAQs
Is Llama 4 Scout 17B 16E Instruct or Kimi K2.5 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Llama 4 Scout 17B 16E Instruct and Kimi K2.5, 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 K2.5?+
Llama 4 Scout 17B 16E Instruct is $0.10 and Kimi K2.5 is $0.45 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 K2.5 is $2.25 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 K2.5?+
Llama 4 Scout 17B 16E Instruct has the larger sourced context window. Llama 4 Scout 17B 16E Instruct supports 10,000K and Kimi K2.5 supports 262K.
Which performs better in benchmarks, Llama 4 Scout 17B 16E Instruct or Kimi K2.5?+
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 K2.5 be self-hosted?+
Both models have the same recorded self-hosting status: supported. Llama 4 Scout 17B 16E Instruct is open weight; Kimi K2.5 is open weight.
Can Llama 4 Scout 17B 16E Instruct and Kimi K2.5 understand images?+
Llama 4 Scout 17B 16E Instruct is documented with image input; Kimi K2.5 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 K2.5?+
Neither has a larger sourced maximum output. Llama 4 Scout 17B 16E Instruct is — and Kimi K2.5 is —.
Do Llama 4 Scout 17B 16E Instruct and Kimi K2.5 support reasoning and tool use?+
Llama 4 Scout 17B 16E Instruct: tool calling and image input. Kimi K2.5: 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 K2.5?+
Llama 4 Scout 17B 16E Instruct has 4 sourced provider routes; Kimi K2.5 has 3, so Llama 4 Scout 17B 16E Instruct has broader tracked availability.
Which offers better value, Llama 4 Scout 17B 16E Instruct or Kimi K2.5?+
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.