Phi-4 Multimodal Instruct vs GLM 5.3
Model Markets Rankings
Intelligence, Cost, and Efficiency
| Ranking | Phi-4-multimodal-instruct | GLM-5.3 |
|---|---|---|
| IntelligenceHigher is better · MM Intelligence v1.4 | UnrankedNot in the 46-model eligible cohort | #16 of 4670.1 score · 2/3-source partial |
| CostLower is better · Published-token output estimate | UnrankedNot in the 45-model eligible cohort | #35 of 45$0.248 per LiveBench case |
| EfficiencyHigher is better · MM Efficiency v1.1 | UnrankedNot in the 38-model eligible cohort | #26 of 3848.8 score · 2/3-source intelligence |
Ranks come from current available eligible cohorts. Intelligence may use a labeled two-of-three partial core. Green highlights appear only when both models are ranked in the same metric, and the three dimensions are not collapsed into an overall winner.
Benchmark Performance
Available Benchmarks
Technical Differences
Side-by-Side Facts
| Field | Phi-4-multimodal-instruct | GLM-5.3 |
|---|---|---|
| Developer | Microsoft | Z.ai |
| Family | Phi 4 Multimodal Instruct | Glm 5 3 |
| Model | Phi-4-multimodal-instruct | GLM-5.3 |
| Version | Phi-4-multimodal-instruct | GLM-5.3 |
| Lifecycle | active | active |
| Released | 2025-02-26 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text, Image, Audio | Text |
| Output modalities | Text | Text |
| Context window | 131K | 1,000K |
| Total parameters | 5.6B | Unknown |
| Active parameters | Unknown | Unknown |
| License | mit | Unknown |
| Open weights | Yes | No |
| API available | Unknown | Yes |
| Self-hostable | Yes | No |
| Provider access | Unknown | Deepinfra (Standard), Fireworks Ai (Standard), Together Ai (Standard), Z.ai (Standard) |
| Capabilities | chat, generation | agents, chat, reasoning, structured_outputs, tools |
11 comparable fields · 9 material differences · Pair passes the primary-source comparison gate
Phi-4 Multimodal Instruct Capabilities
GLM 5.3 Capabilities
Internal Comparison Graph
Related Comparisons
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Primary Evidence
Sources and Freshness
Questions
Phi-4 Multimodal Instruct vs GLM 5.3 FAQs
Is Phi-4 Multimodal Instruct or GLM 5.3 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both Phi-4 Multimodal Instruct and GLM 5.3, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, Phi-4 Multimodal Instruct or GLM 5.3?+
Only GLM 5.3 has a directly sourced input price: $1.20 per million tokens. Only GLM 5.3 has a directly sourced output price: $4.00 per million tokens.
Which has a larger context window, Phi-4 Multimodal Instruct or GLM 5.3?+
GLM 5.3 has the larger sourced context window. Phi-4 Multimodal Instruct supports 131K and GLM 5.3 supports 1,000K.
Which performs better in benchmarks, Phi-4 Multimodal Instruct or GLM 5.3?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can Phi-4 Multimodal Instruct or GLM 5.3 be self-hosted?+
Phi-4 Multimodal Instruct is the only model in this pair currently marked as self-hostable. Phi-4 Multimodal Instruct is open weight; GLM 5.3 is not marked open weight.
Can Phi-4 Multimodal Instruct and GLM 5.3 understand images?+
Phi-4 Multimodal Instruct is documented with image input; GLM 5.3 is not documented with image input. This reflects supported input modalities, not vision quality.
Which can generate longer answers, Phi-4 Multimodal Instruct or GLM 5.3?+
Neither has a larger sourced maximum output. Phi-4 Multimodal Instruct is — and GLM 5.3 is 131K.
Do Phi-4 Multimodal Instruct and GLM 5.3 support reasoning and tool use?+
Phi-4 Multimodal Instruct: image input. GLM 5.3: reasoning and tool calling. Feature support does not establish relative quality.
Which is available from more inference providers, Phi-4 Multimodal Instruct or GLM 5.3?+
Phi-4 Multimodal Instruct has 0 sourced provider routes; GLM 5.3 has 4, so GLM 5.3 has broader tracked availability.
Which offers better value, Phi-4 Multimodal Instruct or GLM 5.3?+
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.