Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Phi-4 Mini Instruct | GLM-4.5V (Non-reasoning) |
|---|---|---|
| Input ($/M tokens) | $0 | $0.6 |
| Output ($/M tokens) | $0 | $1.8 |
Verdict. Phi-4 Mini Instruct and GLM-4.5V (Non-reasoning) split the benchmark comparison evenly at 1–1. The tiebreaker here is price, speed, and what you actually run them on.
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 Mini Instruct is strongest on Intelligence Index (5.7), Coding Index (3.8). 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 43 tok/s — about 54% faster. On time-to-first-token, Phi-4 Mini Instruct responds in 830ms 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 Mini 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 Mini 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 Mini Instruct ≈ $0/run, GLM-4.5V (Non-reasoning) ≈ $300/run. Phi-4 Mini 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 Mini 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).
It's a tie — both models win 1 benchmarks each across 12 evaluated categories. See the detailed benchmark chart above for per-category results.
GLM-4.5V (Non-reasoning) generates tokens faster at 92 tok/s vs 43 tok/s. Phi-4 Mini Instruct also has lower time-to-first-token (0.83s vs 1.85s).
Choose based on your priorities: Phi-4 Mini Instruct for lower cost, both perform similarly on benchmarks, and GLM-4.5V (Non-reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.