Intelligence
59.7
60
General reasoning and benchmark headroom.
CompetitiveKimi K3 (max) is a mid-range-priced text-first model from Kimi with heavy runtime profile, partial context coverage, and the clearest fit around agent workflows / coding.
Benchmark blend
Dev workflow signal
N/A
Mid-range tier
Kimi K3 (max) currently reads as a mid-range text-first option with partially published context and a heavy runtime profile.
Decision Strip
Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.
General reasoning and benchmark headroom.
CompetitiveTTFT 2.58s
LimitedHow much prompt and task state can stay in view.
Unavailable$15.00 output / 1M
CompetitiveEditorial Profile
Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.
The Kimi K3 (max) AI model by Kimi.
Kimi text-first profile
Agent workflows / Coding with partially published context and heavy runtime.
Balanced spend profile. Easier to justify in mixed production and exploration workloads.
Coding indicators point to a strong developer workflow fit.
Costs look manageable, but still deserve attention in always-on agents or batch jobs.
Latency profile is better for deliberate runs than rapid back-and-forth chat.
Current metadata points to a text-first profile rather than a broad multimodal one.
Context limits are only partially published, so long-session planning needs extra validation.
Code generation, refactors, test writing, and tool-assisted debugging.
Benchmarks
Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.
Broad reasoning, knowledge depth, and flagship benchmark posture.
Software implementation, debugging quality, and coding benchmark signal.
Long-horizon execution quality and interactive benchmark evidence.
Extra benchmark cuts are not available for this category yet.
Specs & Pricing
Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.
This model sits in a balanced spend range. It is easier to justify across both production and exploratory workflows.
Metadata
Verification details remain available, but the page no longer forces them ahead of the editorial read.