Kimi K2 Thinking vs Jev
At a Glance
| Compare |  Kimi K2 ThinkingMoonshot AI |  JevTypeSafe |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.60Openrouter ↗ · Sep 22, 2026 | $0.042TypeSafe ↗ · Sep 17, 2026 |
| Output priceFrom · USD / 1M tokens | $2.50Openrouter ↗ · Sep 22, 2026 | $0.000TypeSafe ↗ · Sep 17, 2026 |
| Context windowMaximum documented tokens | 262K | 64K |
| Model facts checked | Aug 28, 2026View model evidence → | Sep 17, 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
Side-by-Side Facts
| Field | Kimi-K2-Thinking | Jev |
|---|---|---|
| Developer | Moonshot AI | TypeSafe |
| Family | Kimi K2 Thinking | Jev |
| Model | Kimi-K2-Thinking | Jev |
| Version | Kimi-K2-Thinking | Jev |
| Lifecycle | active | active |
| Released | 2025-11-06 | 2026-09-15 |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text, Model-specific input |
| Output modalities | Text | Model-specific input |
| Context window | 262K | 64K |
| Total parameters | 1T | Unknown |
| Active parameters | 32B | Unknown |
| License | other | Unknown |
| Open weights | Yes | No |
| API available | Yes | Yes |
| Self-hostable | Yes | No |
| Provider access | Hugging Face (Standard), Openrouter (Standard) | TypeSafe (Standard) |
| Capabilities | chat, generation, reasoning, tools | calibrated-confidence, parallel-evaluation, structured_outputs, typed-decisions |
| Maximum Choice cardinality | Unknown | 255 options |
| Default request rate limit | Unknown | 1200 requests per minute |
| State plus longest question limit | Unknown | 32000 tokens |
| Combined state and questions limit | Unknown | 64000 tokens |
| Default token rate limit | Unknown | 250000 tokens per second |
Kimi K2 Thinking Capabilities
Jev Capabilities
Related Comparisons
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Kimi-K2-ThinkingMoonshot AI | vs |  GLM-5.3Z.ai | cross-developer peerstext |
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Primary Evidence
Sources and Freshness
Questions
Kimi K2 Thinking vs Jev FAQs
Is Kimi K2 Thinking or Jev better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Kimi K2 Thinking and Jev, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Kimi K2 Thinking or Jev?+
Kimi K2 Thinking is $0.60 and Jev is $0.042 per million tokens, so Jev is cheaper on this metric. Kimi K2 Thinking is $2.50 and Jev is $0.000 per million tokens, so Jev is cheaper on this metric.
Which has a larger context window, Kimi K2 Thinking or Jev?+
Kimi K2 Thinking has the larger sourced context window. Kimi K2 Thinking supports 262K and Jev supports 64K.
Which performs better in benchmarks, Kimi K2 Thinking or Jev?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Kimi K2 Thinking or Jev be self-hosted?+
Kimi K2 Thinking is the only model in this pair currently marked as self-hostable. Kimi K2 Thinking is open weight; Jev is not marked open weight.
Can Kimi K2 Thinking and Jev understand images?+
Kimi K2 Thinking is not documented with image input; Jev is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Kimi K2 Thinking or Jev?+
Neither has a larger sourced maximum output. Kimi K2 Thinking is 131K and Jev is —.
Do Kimi K2 Thinking and Jev support reasoning and tool use?+
Kimi K2 Thinking: reasoning and tool calling. Jev: none of these features are definitively sourced. Feature support does not establish relative quality.
Which is available from more inference providers, Kimi K2 Thinking or Jev?+
Kimi K2 Thinking has 2 sourced provider routes; Jev has 1, so Kimi K2 Thinking has broader tracked availability.
Which offers better value, Kimi K2 Thinking or Jev?+
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.