Granite Embedding 278m Multilingual vs GLM 5
At a Glance
| Compare | GLM 5Z.ai | |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.106IBM watsonx.ai ↗ · Aug 29, 2026 | $0.60Deepinfra ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | Not reported | $1.92Openrouter ↗ · Aug 28, 2026 |
| Context windowMaximum documented tokens | 1K | 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 | granite-embedding-278m-multilingual | GLM-5 |
|---|---|---|
| Developer | IBM | Z.ai |
| Family | Granite Embedding 278m Multilingual | Glm 5 |
| Model | granite-embedding-278m-multilingual | GLM-5 |
| Version | granite-embedding-278m-multilingual | GLM-5 |
| Lifecycle | active | active |
| Released | 2024-12-18 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Embedding | Text |
| Context window | 1K | 203K |
| Total parameters | 278M | 753.9B |
| Active parameters | Unknown | Unknown |
| License | apache-2.0 | mit |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Hugging Face (Standard), IBM watsonx.ai (Pay as you go) | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | embeddings | chat, generation, reasoning, tools |
Granite Embedding 278m Multilingual Capabilities
GLM 5 Capabilities
Primary Evidence
Sources and Freshness
Questions
Granite Embedding 278m Multilingual vs GLM 5 FAQs
Is Granite Embedding 278m Multilingual or GLM 5 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Granite Embedding 278m Multilingual and GLM 5, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Granite Embedding 278m Multilingual or GLM 5?+
Granite Embedding 278m Multilingual is $0.106 and GLM 5 is $0.60 per million tokens, so Granite Embedding 278m Multilingual is cheaper on this metric. Only GLM 5 has a directly sourced output price: $1.92 per million tokens.
Which has a larger context window, Granite Embedding 278m Multilingual or GLM 5?+
GLM 5 has the larger sourced context window. Granite Embedding 278m Multilingual supports 1K and GLM 5 supports 203K.
Which performs better in benchmarks, Granite Embedding 278m Multilingual or GLM 5?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Granite Embedding 278m Multilingual or GLM 5 be self-hosted?+
Both models have the same recorded self-hosting status: supported. Granite Embedding 278m Multilingual is open weight; GLM 5 is open weight.
Can Granite Embedding 278m Multilingual and GLM 5 understand images?+
Granite Embedding 278m Multilingual 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, Granite Embedding 278m Multilingual or GLM 5?+
Neither has a larger sourced maximum output. Granite Embedding 278m Multilingual is — and GLM 5 is —.
Do Granite Embedding 278m Multilingual and GLM 5 support reasoning and tool use?+
Granite Embedding 278m Multilingual: none of these features are definitively sourced. GLM 5: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Granite Embedding 278m Multilingual or GLM 5?+
Granite Embedding 278m Multilingual has 2 sourced provider routes; GLM 5 has 4, so GLM 5 has broader tracked availability.
Which offers better value, Granite Embedding 278m Multilingual 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.