Qwen3.8 Max vs GLM OCR
At a Glance
| Compare |  Qwen3.8 MaxQwen |  GLM OCRZ.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #10 of 4679.2 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 52.8–86.1 | UnrankedNot in the 46-model eligible cohort |
| CostLower is better · Published-token output estimate | #23 of 44$0.107 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| EfficiencyHigher is better · MM Efficiency v1.5 | #6 of 3863.2 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 50.0–66.7 | UnrankedNot in the 38-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $1.65Deepinfra ↗ · Sep 22, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $4.951Deepinfra ↗ · Sep 22, 2026 | Not reported |
| Context windowMaximum documented tokens | 1,000K | 131K |
| 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
Side-by-Side Facts
| Field | Qwen3.8-Max | GLM-OCR |
|---|---|---|
| Developer | Qwen | Z.ai |
| Family | Qwen3 8 Max | Glm OCR |
| Model | Qwen3.8-Max | GLM-OCR |
| Version | Qwen3.8-Max | GLM-OCR |
| Lifecycle | active | active |
| Released | Unknown | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Video | Text, Image |
| Output modalities | Text | Text |
| Context window | 1,000K | 131K |
| Total parameters | 2.4T | 1.3B |
| Active parameters | Unknown | Unknown |
| License | Unknown | mit |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Alibaba Cloud Model Studio (Standard), Deepinfra (Standard), Fireworks Ai (Standard), Openrouter (Standard) | Together Ai (Standard) |
| Capabilities | agents, chat, reasoning, structured_outputs, tools, vision | chat, generation, tools |
Qwen3.8 Max Capabilities
GLM OCR Capabilities
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| vs | Qwen3.8-MaxQwen | cross-developer peersimage, text |
Primary Evidence
Sources and Freshness
Questions
Qwen3.8 Max vs GLM OCR FAQs
Is Qwen3.8 Max or GLM OCR better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Qwen3.8 Max and GLM OCR, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Qwen3.8 Max or GLM OCR?+
Only Qwen3.8 Max has a directly sourced input price: $1.65 per million tokens. Only Qwen3.8 Max has a directly sourced output price: $4.951 per million tokens.
Which has a larger context window, Qwen3.8 Max or GLM OCR?+
Qwen3.8 Max has the larger sourced context window. Qwen3.8 Max supports 1,000K and GLM OCR supports 131K.
Which performs better in benchmarks, Qwen3.8 Max or GLM OCR?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Qwen3.8 Max or GLM OCR be self-hosted?+
GLM OCR is the only model in this pair currently marked as self-hostable. Qwen3.8 Max is not marked open weight; GLM OCR is open weight.
Can Qwen3.8 Max and GLM OCR understand images?+
Qwen3.8 Max 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, Qwen3.8 Max or GLM OCR?+
Neither has a larger sourced maximum output. Qwen3.8 Max is 131K and GLM OCR is —.
Do Qwen3.8 Max and GLM OCR support reasoning and tool use?+
Qwen3.8 Max: 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, Qwen3.8 Max or GLM OCR?+
Qwen3.8 Max has 4 sourced provider routes; GLM OCR has 1, so Qwen3.8 Max has broader tracked availability.
Which offers better value, Qwen3.8 Max 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.