Claude Opus 4.8 vs Kimi K2.6
At a Glance
| Compare | Claude Opus 4.8Anthropic | Kimi K2.6Moonshot AI |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #18 of 4670.1 score · 3/3 sources · complete | #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 | #42 of 44$0.604 per LiveBench case | #21 of 44$0.095 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.5 | #35 of 3840.5 score · 3/3 sources · complete | #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 | $5.00Anthropic ↗ · Sep 3, 2026 | $0.75Deepinfra ↗ · Sep 22, 2026 |
| Output priceFrom · USD / 1M tokens | $25.00Anthropic ↗ · Sep 3, 2026 | $3.50Deepinfra ↗ · Sep 22, 2026 |
| Context windowMaximum documented tokens | 1,000K | 262K |
| Model facts checked | Aug 29, 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 | Claude Opus 4.8 | Kimi-K2.6 |
|---|---|---|
| LMArena Document Arenadocument-2026-09-13-d25aabda0010 · arena_rating · statistical tie | 1,464.07100% of row best · rating · claude-opus-4-8; 95% CI [1457.10320334, 1471.03216030]; votes 12128; rank 19 | 1,450.7499% of row best · rating · kimi-k2.6; 95% CI [1443.10467262, 1458.37474735]; votes 11291; rank 27 |
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · statistical tie | 1,452.66100% of row best · rating · claude-opus-4-8; 95% CI [1448.52490690, 1456.79817098]; votes 53446; rank 40 | 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 · statistical tie | 1,288.23100% of row best · rating · claude-opus-4-8; 95% CI [1281.27991800, 1295.18871241]; votes 15801; rank 25 | 1,280.4099% of row best · rating · kimi-k2.6; 95% CI [1273.38025179, 1287.42397051]; votes 15347; rank 28 |
| LiveBench2026-06-25 · overall · leader | 81.18100% of row best · percent · claude-opus-4-8-max-effort · 24,171 output tokens / case | 74.1891% of row best · percent · kimi-k2.6-thinking · 27,001 output tokens / case |
| ToneBench2026-09-11-10-task-4ef099199c9c · overall_score · unverified | 87.91100% of row best · points · Claude Opus 4.8 (max effort) · 24,846 output tokens / case | 82.2494% of row best · points · Kimi K2.6 (thinking) · 18,080 output tokens / case |
| Overall ResultCounted from the protocol-matched rows above · 3 ties | 1 benchmark winNo overall winner | 0 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 | Claude Opus 4.8 | Kimi-K2.6 |
|---|---|---|
| Developer | Anthropic | Moonshot AI |
| Family | Claude 4 8 | Kimi K2 6 |
| Model | Claude Opus 4.8 | Kimi-K2.6 |
| Version | Claude Opus 4.8 | Kimi-K2.6 |
| Lifecycle | active | active |
| Released | 2026-05-28 | 2026-04-20 |
| Knowledge cutoff | 2026-01-01 | Unknown |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,000K | 262K |
| Total parameters | Unknown | 1T |
| Active parameters | Unknown | 32B |
| License | Unknown | other |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Anthropic (Standard), Deepinfra (Standard) | Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, reasoning, tools |
Claude Opus 4.8 Capabilities
Kimi K2.6 Capabilities
Primary Evidence
Sources and Freshness
Questions
Claude Opus 4.8 vs Kimi K2.6 FAQs
Is Claude Opus 4.8 or Kimi K2.6 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Opus 4.8 and Kimi K2.6, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Claude Opus 4.8 or Kimi K2.6?+
Claude Opus 4.8 is $5.00 and Kimi K2.6 is $0.75 per million tokens, so Kimi K2.6 is cheaper on this metric. Claude Opus 4.8 is $25.00 and Kimi K2.6 is $3.50 per million tokens, so Kimi K2.6 is cheaper on this metric.
Which has a larger context window, Claude Opus 4.8 or Kimi K2.6?+
Claude Opus 4.8 has the larger sourced context window. Claude Opus 4.8 supports 1,000K and Kimi K2.6 supports 262K.
Which performs better in benchmarks, Claude Opus 4.8 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 Claude Opus 4.8 or Kimi K2.6 be self-hosted?+
Kimi K2.6 is the only model in this pair currently marked as self-hostable. Claude Opus 4.8 is not marked open weight; Kimi K2.6 is open weight.
Can Claude Opus 4.8 and Kimi K2.6 understand images?+
Claude Opus 4.8 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, Claude Opus 4.8 or Kimi K2.6?+
Neither has a larger sourced maximum output. Claude Opus 4.8 is 128K and Kimi K2.6 is —.
Do Claude Opus 4.8 and Kimi K2.6 support reasoning and tool use?+
Claude Opus 4.8: reasoning, 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, Claude Opus 4.8 or Kimi K2.6?+
Claude Opus 4.8 has 2 sourced provider routes; Kimi K2.6 has 5, so Kimi K2.6 has broader tracked availability.
Which offers better value, Claude Opus 4.8 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.