Side-by-side comparison of pricing, 12 benchmarks, and generation speed.
| Metric | Olmo 3.1 32B Think | GLM-4.5V (Non-reasoning) |
|---|---|---|
| Input ($/M tokens) | $0 | $0.6 |
| Output ($/M tokens) | $0 | $1.8 |
Verdict. Olmo 3.1 32B Think wins the overall benchmark matchup 1–0 across 1 overlapping categories, but raw benchmark score is only one input to the decision.
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. Olmo 3.1 32B Think is strongest on Intelligence Index (7.9). GLM-4.5V (Non-reasoning) leads on Intelligence Index (6.8).
Speed. Speed data is incomplete for this pair; benchmark and price should decide.
Provider. Allen Institute for AI and Z AI sell to overlapping but distinct developer audiences: Allen Institute for AI 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): Olmo 3.1 32B Think 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): Olmo 3.1 32B Think ≈ $0.00/run, GLM-4.5V (Non-reasoning) ≈ $6.60/run. At agent/realtime scale (200M input / 100M output per million requests): Olmo 3.1 32B Think ≈ $0/run, GLM-4.5V (Non-reasoning) ≈ $300/run. Olmo 3.1 32B Think 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.
Data from Artificial Analysis API — 12 benchmarks
Olmo 3.1 32B Think 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).
Olmo 3.1 32B Think wins 1 out of 12 benchmarks compared to 0 for GLM-4.5V (Non-reasoning). See the detailed benchmark chart above for per-category results.
GLM-4.5V (Non-reasoning) generates tokens faster at 92 tok/s vs — tok/s. However, GLM-4.5V (Non-reasoning) has lower time-to-first-token (1.85s vs —s).
Choose based on your priorities: Olmo 3.1 32B Think for lower cost, Olmo 3.1 32B Think for stronger benchmark performance, and GLM-4.5V (Non-reasoning) for faster generation. For latency-sensitive apps, check the TTFT comparison above.