Kimi K2.6 vs GLM OCR
At a Glance
| Compare |  Kimi K2.6Moonshot AI |  GLM OCRZ.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #32 of 4650.5 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 33.6–67.0 | UnrankedNot in the 46-model eligible cohort |
| CostLower is better · Published-token output estimate | #21 of 44$0.095 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| EfficiencyHigher is better · MM Efficiency v1.5 | #24 of 3850.1 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 41.7–58.4 | UnrankedNot in the 38-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.75Deepinfra ↗ · Sep 21, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $3.50Deepinfra ↗ · Sep 21, 2026 | Not reported |
| Context windowMaximum documented tokens | 262K | 131K |
| 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
Side-by-Side Facts
| Field | Kimi-K2.6 | GLM-OCR |
|---|---|---|
| Developer | Moonshot AI | Z.ai |
| Family | Kimi K2 6 | Glm OCR |
| Model | Kimi-K2.6 | GLM-OCR |
| Version | Kimi-K2.6 | GLM-OCR |
| Lifecycle | active | active |
| Released | 2026-04-20 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 262K | 131K |
| Total parameters | 1T | 1.3B |
| Active parameters | 32B | Unknown |
| License | other | mit |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Fireworks Ai (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) | Together Ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | chat, generation, tools |
Kimi K2.6 Capabilities
GLM OCR Capabilities
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Primary Evidence
Sources and Freshness
Questions
Kimi K2.6 vs GLM OCR FAQs
Is Kimi K2.6 or GLM OCR better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Kimi K2.6 and GLM OCR, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Kimi K2.6 or GLM OCR?+
Only Kimi K2.6 has a directly sourced input price: $0.75 per million tokens. Only Kimi K2.6 has a directly sourced output price: $3.50 per million tokens.
Which has a larger context window, Kimi K2.6 or GLM OCR?+
Kimi K2.6 has the larger sourced context window. Kimi K2.6 supports 262K and GLM OCR supports 131K.
Which performs better in benchmarks, Kimi K2.6 or GLM OCR?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Kimi K2.6 or GLM OCR be self-hosted?+
Both models have the same recorded self-hosting status: supported. Kimi K2.6 is open weight; GLM OCR is open weight.
Can Kimi K2.6 and GLM OCR understand images?+
Kimi K2.6 is documented with image input; GLM OCR is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Kimi K2.6 or GLM OCR?+
Neither has a larger sourced maximum output. Kimi K2.6 is — and GLM OCR is —.
Do Kimi K2.6 and GLM OCR support reasoning and tool use?+
Kimi K2.6: reasoning, tool calling, and image input. GLM OCR: tool calling and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Kimi K2.6 or GLM OCR?+
Kimi K2.6 has 5 sourced provider routes; GLM OCR has 1, so Kimi K2.6 has broader tracked availability.
Which offers better value, Kimi K2.6 or GLM OCR?+
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.