Olmo 3 7B Instruct vs GLM 5
At a Glance
| Compare |  GLM 5Z.ai | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | Not reported | $0.60Deepinfra ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $1.92Openrouter ↗ · Aug 28, 2026 |
| Context windowMaximum documented tokens | 66K | 203K |
| 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 | Olmo-3-7B-Instruct | GLM-5 |
|---|---|---|
| Developer | Ai2 | Z.ai |
| Family | Olmo 3 7b Instruct | Glm 5 |
| Model | Olmo-3-7B-Instruct | GLM-5 |
| Version | Olmo-3-7B-Instruct | GLM-5 |
| Lifecycle | active | active |
| Released | Unknown | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 66K | 203K |
| Total parameters | 7.3B | 753.9B |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | mit |
| Open weights | Yes | Yes |
| API available | Unknown | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Unknown | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation, tools | chat, generation, reasoning, tools |
Olmo 3 7B Instruct Capabilities
GLM 5 Capabilities
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| vs |  DeepSeek-V4.1-FlashDeepSeek | cross-developer peerstext | |
| vs | Grok 4.6xAI | cross-developer peerstext | |
| vs |  Qwen3.8-MaxQwen | cross-developer peerstext | |
| vs |  Kimi-K3Moonshot AI | cross-developer peerstext | |
 MiniMax-M3MiniMax | vs | cross-developer peerstext | |
| vs | GLM-5.3Z.ai | cross-developer peerstext | |
| vs |  Hy4 previewTencent | cross-developer peerstext | |
| vs | Seed 2.1 ProByteDance Seed | cross-developer peerstext | |
| vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
Olmo 3 7B Instruct vs GLM 5 FAQs
Is Olmo 3 7B Instruct or GLM 5 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Olmo 3 7B Instruct and GLM 5, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Olmo 3 7B Instruct or GLM 5?+
Only GLM 5 has a directly sourced input price: $0.60 per million tokens. Only GLM 5 has a directly sourced output price: $1.92 per million tokens.
Which has a larger context window, Olmo 3 7B Instruct or GLM 5?+
GLM 5 has the larger sourced context window. Olmo 3 7B Instruct supports 66K and GLM 5 supports 203K.
Which performs better in benchmarks, Olmo 3 7B Instruct or GLM 5?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Olmo 3 7B Instruct or GLM 5 be self-hosted?+
Both models have the same recorded self-hosting status: supported. Olmo 3 7B Instruct is open weight; GLM 5 is open weight.
Can Olmo 3 7B Instruct and GLM 5 understand images?+
Olmo 3 7B Instruct is not documented with image input; GLM 5 is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Olmo 3 7B Instruct or GLM 5?+
Neither has a larger sourced maximum output. Olmo 3 7B Instruct is 33K and GLM 5 is —.
Do Olmo 3 7B Instruct and GLM 5 support reasoning and tool use?+
Olmo 3 7B Instruct: tool calling. GLM 5: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Olmo 3 7B Instruct or GLM 5?+
Olmo 3 7B Instruct has 0 sourced provider routes; GLM 5 has 4, so GLM 5 has broader tracked availability.
Which offers better value, Olmo 3 7B Instruct or GLM 5?+
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.