Claude Opus 4.5 vs GLM OCR
At a Glance
| Compare | Claude Opus 4.5Anthropic |  GLM OCRZ.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #27 of 4655.4 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 36.9–70.3 | UnrankedNot in the 46-model eligible cohort |
| CostLower is better · Published-token output estimate | #40 of 44$0.380 per LiveBench case | UnrankedNot in the 44-model eligible cohort |
| EfficiencyHigher is better · MM Efficiency v1.5 | #36 of 3838.0 score · 2/3 sources · provisional · missing ARC-AGI-2 · full-core range 28.8–45.4 | UnrankedNot in the 38-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $5.00Anthropic ↗ · Sep 3, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $25.00Anthropic ↗ · Sep 3, 2026 | Not reported |
| Context windowMaximum documented tokens | 200K | 131K |
| Model facts checked | Sep 3, 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 | Claude Opus 4.5 | GLM-OCR |
|---|---|---|
| Developer | Anthropic | Z.ai |
| Family | Claude 4 | Glm OCR |
| Model | Claude Opus 4.5 | GLM-OCR |
| Version | Claude Opus 4.5 | GLM-OCR |
| Lifecycle | active | active |
| Released | 2025-11-24 | Unknown |
| Knowledge cutoff | 2025-05-01 | Unknown |
| Input modalities | Text, Image | Text, Image |
| Output modalities | Text | Text |
| Context window | 200K | 131K |
| Total parameters | Unknown | 1.3B |
| Active parameters | Unknown | Unknown |
| License | Unknown | mit |
| Open weights | No | Yes |
| API available | Yes | Yes |
| Self-hostable | No | Yes |
| Provider access | Anthropic (Standard) | Together Ai (Standard) |
| Capabilities | chat, generation, reasoning, structured_outputs, tools | chat, generation, tools |
Claude Opus 4.5 Capabilities
GLM OCR Capabilities
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Primary Evidence
Sources and Freshness
Questions
Claude Opus 4.5 vs GLM OCR FAQs
Is Claude Opus 4.5 or GLM OCR better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Opus 4.5 and GLM OCR, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Claude Opus 4.5 or GLM OCR?+
Only Claude Opus 4.5 has a directly sourced input price: $5.00 per million tokens. Only Claude Opus 4.5 has a directly sourced output price: $25.00 per million tokens.
Which has a larger context window, Claude Opus 4.5 or GLM OCR?+
Claude Opus 4.5 has the larger sourced context window. Claude Opus 4.5 supports 200K and GLM OCR supports 131K.
Which performs better in benchmarks, Claude Opus 4.5 or GLM OCR?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Claude Opus 4.5 or GLM OCR be self-hosted?+
GLM OCR is the only model in this pair currently marked as self-hostable. Claude Opus 4.5 is not marked open weight; GLM OCR is open weight.
Can Claude Opus 4.5 and GLM OCR understand images?+
Claude Opus 4.5 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, Claude Opus 4.5 or GLM OCR?+
Neither has a larger sourced maximum output. Claude Opus 4.5 is 64K and GLM OCR is —.
Do Claude Opus 4.5 and GLM OCR support reasoning and tool use?+
Claude Opus 4.5: 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, Claude Opus 4.5 or GLM OCR?+
Claude Opus 4.5 has 1 sourced provider route; GLM OCR has 1, a tie.
Which offers better value, Claude Opus 4.5 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.