Intelligence
33.5
34
General reasoning and benchmark headroom.
LimitedMoonshotAI: Kimi K2 Thinking is a budget-priced text-first model from Kimi with partial runtime data, large context posture, and the clearest fit around long-context research / coding.
Benchmark blend
Dev workflow signal
Large
Budget tier
MoonshotAI: Kimi K2 Thinking currently reads as a budget text-first option with large context and a partially published 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.
LimitedLatency data is partial.
UnavailableHow much prompt and task state can stay in view.
Above average$2.50 output / 1M
EfficientEditorial Profile
Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.
Kimi K2 Thinking is Moonshot AI’s most advanced open reasoning model to date, extending the K2 series into agentic, long-horizon reasoning. Built on the trillion-parameter Mixture-of-Experts (MoE) architecture introduced in...
Kimi text-first profile
Long-context research / Coding with large context and partially published runtime.
Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.
Coding indicators point to a strong developer workflow fit.
Large context headroom supports repo-wide prompts and long research sessions.
Budget-friendly input pricing is a strength, but raw capability may vary by workload.
Latency data is incomplete, so interactive responsiveness is harder to rank confidently.
Current metadata points to a text-first profile rather than a broad multimodal one.
Code generation, refactors, test writing, and tool-assisted debugging.
Long-context summarization, repo analysis, and policy or document review.
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.
Formal reasoning, structured problem solving, and competition-style math.
Long-horizon execution quality and interactive benchmark evidence.
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 is relatively efficient on price. It is the easier fit when sustained prompt volume matters.
Metadata
Verification details remain available, but the page no longer forces them ahead of the editorial read.