Claude Haiku 4.5 vs GLM 5V Turbo
At a Glance
| Compare | Claude Haiku 4.5Anthropic |  GLM 5V TurboZ.ai |
|---|---|---|
| Intelligence, Cost, and Efficiency | ||
| IntelligenceHigher is better · MM Intelligence v2.5 | #43 of 469.0 score · 2/3 sources · provisional · missing LiveBench · full-core range 6.0–39.3 | UnrankedNot in the 46-model eligible cohort |
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $1.00Anthropic ↗ · Sep 3, 2026 | Not reported |
| Output priceFrom · USD / 1M tokens | $5.00Anthropic ↗ · Sep 3, 2026 | Not reported |
| Context windowMaximum documented tokens | 200K | 200K |
| Model facts checked | Aug 28, 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 Haiku 4.5 | GLM-5V-Turbo |
|---|---|---|
| Developer | Anthropic | Z.ai |
| Family | Claude 4 5 | Glm 5v |
| Model | Claude Haiku 4.5 | GLM-5V-Turbo |
| Version | Claude Haiku 4.5 | GLM-5V-Turbo |
| Lifecycle | active | active |
| Released | 2025-10-15 | Unknown |
| Knowledge cutoff | Unknown | 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), Deepinfra (Standard) | Z.ai (Standard) |
| Capabilities | chat, generation, reasoning, tools | agents, chat, computer-use, reasoning, tools, vision |
Claude Haiku 4.5 Capabilities
GLM 5V Turbo Capabilities
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Primary Evidence
Sources and Freshness
Questions
Claude Haiku 4.5 vs GLM 5V Turbo FAQs
Is Claude Haiku 4.5 or GLM 5V Turbo better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Claude Haiku 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 Haiku 4.5 or GLM 5V Turbo?+
Only Claude Haiku 4.5 has a directly sourced input price: $1.00 per million tokens. Only Claude Haiku 4.5 has a directly sourced output price: $5.00 per million tokens.
Which has a larger context window, Claude Haiku 4.5 or GLM 5V Turbo?+
Neither model has a larger sourced context window in this comparison. Claude Haiku 4.5 is 200K and GLM 5V Turbo is 200K.
Which performs better in benchmarks, Claude Haiku 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 Haiku 4.5 or GLM 5V Turbo be self-hosted?+
Both models have the same recorded self-hosting status: unsupported. Claude Haiku 4.5 is not marked open weight; GLM 5V Turbo is not marked open weight.
Can Claude Haiku 4.5 and GLM 5V Turbo understand images?+
Claude Haiku 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 Haiku 4.5 or GLM 5V Turbo?+
GLM 5V Turbo has the larger sourced maximum output: Claude Haiku 4.5 supports 64K and GLM 5V Turbo supports 131K output tokens.
Do Claude Haiku 4.5 and GLM 5V Turbo support reasoning and tool use?+
Claude Haiku 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 Haiku 4.5 or GLM 5V Turbo?+
Claude Haiku 4.5 has 2 sourced provider routes; GLM 5V Turbo has 1, so Claude Haiku 4.5 has broader tracked availability.
Which offers better value, Claude Haiku 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.