Claude Sonnet 4.5 vs GLM 5V Turbo
At a Glance
| Compare | Claude Sonnet 4.5Anthropic | GLM 5V TurboZ.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #36 of 4627.7 score · 2/3 sources · provisional · missing LiveBench · full-core range 18.5–51.8 | UnrankedNot in the 46-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $3.00Anthropic ↗ · Sep 3, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $15.00Anthropic ↗ · Sep 3, 2026 | Not reported |
| Context windowMaximum documented tokens | 200K | 200K |
| Model facts checked | Sep 3, 2026View model evidence → | Aug 29, 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 Sonnet 4.5 | GLM-5V-Turbo |
|---|---|---|
| Developer | Anthropic | Z.ai |
| Family | Claude 4 | Glm 5v |
| Model | Claude Sonnet 4.5 | GLM-5V-Turbo |
| Version | Claude Sonnet 4.5 | GLM-5V-Turbo |
| Lifecycle | active | active |
| Released | 2025-09-29 | Unknown |
| Knowledge cutoff | 2025-01-01 | Unknown |
| Input modalities | Text, Image | Text, Image, Video, Document |
| Output modalities | Text | Text |
| Context window | 200K | 200K |
| Total parameters | Unknown | Unknown |
| Active parameters | Unknown | Unknown |
| License | Unknown | Unknown |
| Open weights | No | No |
| API available | Yes | Yes |
| Self-hostable | No | No |
| Provider access | Anthropic (Standard) | Z.ai (Standard) |
| Capabilities | chat, generation, reasoning, structured_outputs, tools | agents, chat, computer-use, reasoning, tools, vision |
Claude Sonnet 4.5 Capabilities
GLM 5V Turbo Capabilities
Primary Evidence
Sources and Freshness
Questions
Claude Sonnet 4.5 vs GLM 5V Turbo FAQs
Is Claude Sonnet 4.5 or GLM 5V Turbo better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Sonnet 4.5 and GLM 5V Turbo, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Claude Sonnet 4.5 or GLM 5V Turbo?+
Only Claude Sonnet 4.5 has a directly sourced input price: $3.00 per million tokens. Only Claude Sonnet 4.5 has a directly sourced output price: $15.00 per million tokens.
Which has a larger context window, Claude Sonnet 4.5 or GLM 5V Turbo?+
Neither model has a larger sourced context window in this comparison. Claude Sonnet 4.5 is 200K and GLM 5V Turbo is 200K.
Which performs better in benchmarks, Claude Sonnet 4.5 or GLM 5V Turbo?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Claude Sonnet 4.5 or GLM 5V Turbo be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Claude Sonnet 4.5 is not marked open weight; GLM 5V Turbo is not marked open weight.
Can Claude Sonnet 4.5 and GLM 5V Turbo understand images?+
Claude Sonnet 4.5 is documented with image input; GLM 5V Turbo is documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Claude Sonnet 4.5 or GLM 5V Turbo?+
GLM 5V Turbo has the larger sourced maximum output: Claude Sonnet 4.5 supports 64K and GLM 5V Turbo supports 131K output tokens.
Do Claude Sonnet 4.5 and GLM 5V Turbo support reasoning and tool use?+
Claude Sonnet 4.5: reasoning, tool calling, and image input. GLM 5V Turbo: reasoning, tool calling, and image input. Feature support does not establish relative quality.
Which is available from more inference providers, Claude Sonnet 4.5 or GLM 5V Turbo?+
Claude Sonnet 4.5 has 1 sourced provider route; GLM 5V Turbo has 1, a tie.
Which offers better value, Claude Sonnet 4.5 or GLM 5V Turbo?+
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.