phi-4 vs GLM 5
At a Glance
| Compare |  phi-4Microsoft |  GLM 5Z.ai |
|---|---|---|
| Pricing and Limits | ||
| Input priceFrom · USD / 1M tokens | $0.070Deepinfra ↗ · Sep 21, 2026 | $0.60Deepinfra ↗ · Sep 3, 2026 |
| Output priceFrom · USD / 1M tokens | $0.14Deepinfra ↗ · Sep 21, 2026 | $1.92Openrouter ↗ · Aug 28, 2026 |
| Context windowMaximum documented tokens | 16K | 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
| Benchmark | phi-4 | GLM-5 |
|---|---|---|
| LMArena Text Arenatext-2026-09-13-d25aabda0010 · arena_rating · leader | 1,216.6084% of row best · rating · phi-4; 95% CI [1212.02298793, 1221.18318193]; votes 24126; rank 300 | 1,446.27100% of row best · rating · glm-5; 95% CI [1441.95071870, 1450.59229773]; votes 27605; rank 51 |
| Overall ResultCounted from the protocol-matched rows above | 0 benchmark winsNo overall winner | 1 benchmark winNo overall winner |
Third-party benchmark Only like-for-like primary-publisher results are shown; raw scores, relative scores, configuration, and token spend remain visible.
Side-by-Side Facts
| Field | phi-4 | GLM-5 |
|---|---|---|
| Developer | Microsoft | Z.ai |
| Family | Phi 4 | Glm 5 |
| Model | phi-4 | GLM-5 |
| Version | phi-4 | GLM-5 |
| Lifecycle | active | active |
| Released | 2024-12-12 | Unknown |
| Knowledge cutoff | Unknown | Unknown |
| Input modalities | Text | Text |
| Output modalities | Text | Text |
| Context window | 16K | 203K |
| Total parameters | 14.7B | 753.9B |
| Active parameters | Unknown | Unknown |
| License | mit | mit |
| Open weights | Yes | Yes |
| API available | Yes | Yes |
| Self-hostable | Yes | Yes |
| Provider access | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard) | Deepinfra (Standard), Hugging Face (Standard), Openrouter (Standard), Together Ai (Standard) |
| Capabilities | chat, generation | chat, generation, reasoning, tools |
phi-4 Capabilities
GLM 5 Capabilities
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phi-4Microsoft | vs |  Mistral Large 3Mistral AI | cross-developer peerstext |
Primary Evidence
Sources and Freshness
Questions
phi-4 vs GLM 5 FAQs
Is phi-4 or GLM 5 better for coding?+
This comparison does not currently contain a protocol-matched coding benchmark for both phi-4 and GLM 5, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.
Which is cheaper, phi-4 or GLM 5?+
phi-4 is $0.070 and GLM 5 is $0.60 per million tokens, so phi-4 is cheaper on this metric. phi-4 is $0.14 and GLM 5 is $1.92 per million tokens, so phi-4 is cheaper on this metric.
Which has a larger context window, phi-4 or GLM 5?+
GLM 5 has the larger sourced context window. phi-4 supports 16K and GLM 5 supports 203K.
Which performs better in benchmarks, phi-4 or GLM 5?+
There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.
Can phi-4 or GLM 5 be self-hosted?+
Both models have the same recorded self-hosting status: supported. phi-4 is open weight; GLM 5 is open weight.
Can phi-4 and GLM 5 understand images?+
phi-4 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, phi-4 or GLM 5?+
Neither has a larger sourced maximum output. phi-4 is — and GLM 5 is —.
Do phi-4 and GLM 5 support reasoning and tool use?+
phi-4: 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, phi-4 or GLM 5?+
phi-4 has 3 sourced provider routes; GLM 5 has 4, so GLM 5 has broader tracked availability.
Which offers better value, phi-4 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.