Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Phi-4 Multimodal Instruct | GLM-4.5V (Non-reasoning) |
|---|---|---|
| Input ($/M tokens) | $0 | $0.6 |
| Output ($/M tokens) | $0 | $1.8 |
Verdict. GLM-4.5V (Non-reasoning) takes the aggregate benchmark matchup 1–0 across 1 categories. Real workloads usually care about a handful of specific tasks — see the per-benchmark table above.
Pricing. Pricing varies significantly between these models — check the table above for the exact per-token rates. Many production workloads actually surface input-token cost (retrieval-augmented prompts, code-context windows), so factor both directions.
Strengths. Phi-4 Multimodal Instruct is strongest on Intelligence Index (4.2). GLM-4.5V (Non-reasoning) leads on Intelligence Index (6.8).
Speed. On throughput, GLM-4.5V (Non-reasoning) generates tokens at 92 tok/s versus 17 tok/s — about 81% faster. On time-to-first-token, Phi-4 Multimodal Instruct responds in 880ms vs 1850ms, which matters most for chat-style UIs.
Provider. Microsoft and Z AI sell to overlapping but distinct developer audiences: Microsoft tends to ship frontier reasoning models with premium positioning, while Z AI often prices more aggressively. Your existing vendor relationships, billing, and SLA preferences may matter as much as the raw numbers above.
Workload cost. Workload scenarios (per million requests at 30M input + 15M output tokens): Phi-4 Multimodal Instruct costs $0.00 ($0/year); GLM-4.5V (Non-reasoning) costs $45.00 ($540/year). At a smaller 5M-input/2M-output scale (single-developer tool or prototype): Phi-4 Multimodal Instruct ≈ $0.00/run, GLM-4.5V (Non-reasoning) ≈ $6.60/run. At agent/realtime scale (200M input / 100M output per million requests): Phi-4 Multimodal Instruct ≈ $0/run, GLM-4.5V (Non-reasoning) ≈ $300/run. Phi-4 Multimodal Instruct becomes more attractive at higher volume — the absolute per-token pricing difference compounds when you ship at scale.
Recommendation. Both models have legitimate use cases — the right answer depends on whether you are optimizing for benchmark ceiling, latency, or unit cost. Start with the cheaper / faster model, evaluate against your specific task, and only switch if the upgrade shows a meaningful lift.
Head-to-head deltas
Data from Artificial Analysis API — 12 benchmarks
Phi-4 Multimodal Instruct is cheaper overall. Its blended price (3:1 input/output ratio) is $0.00/M tokens vs $0.90/M for GLM-4.5V (Non-reasoning).
GLM-4.5V (Non-reasoning) wins 1 out of 12 benchmarks compared to 0 for Phi-4 Multimodal Instruct. See the detailed benchmark chart above for per-category results.
GLM-4.5V (Non-reasoning) generates tokens faster at 92 tok/s vs 17 tok/s. Phi-4 Multimodal Instruct also has lower time-to-first-token (0.88s vs 1.85s).
Choose based on your priorities: Phi-4 Multimodal Instruct for lower cost, GLM-4.5V (Non-reasoning) for stronger benchmark performance, and GLM-4.5V (Non-reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.