GLM-5V-Turbo vs Phi-4 Reasoning Vision 15B
GLM-5V-Turbo (2026) and Phi-4 Reasoning Vision 15B (2026) are frontier reasoning models from Zhipu AI and Microsoft Research. GLM-5V-Turbo ships a 200k-token context window, while Phi-4 Reasoning Vision 15B ships a not-yet-sourced context window. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads. It focuses on practical selection signals rather than broad model-family marketing.
GLM-5V-Turbo is safer overall; choose Phi-4 Reasoning Vision 15B when vision-heavy evaluation matters.
Decision scorecard
Local evidence first| Signal | GLM-5V-Turbo | Phi-4 Reasoning Vision 15B |
|---|---|---|
| Best for | reasoning-heavy apps, multimodal apps, and tool-calling agents | multimodal apps |
| Decision fit | RAG, Agents, and Long context | Vision |
| Context window | 200k | — |
| Cheapest output | $4/1M tokens | - |
| Provider routes | 2 tracked | 0 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- GLM-5V-Turbo has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GLM-5V-Turbo has broader tracked provider coverage for fallback and procurement flexibility.
- GLM-5V-Turbo uniquely exposes Reasoning, Function calling, and Tool use in local model data.
- Local decision data tags GLM-5V-Turbo for RAG, Agents, and Long context.
- Local decision data tags Phi-4 Reasoning Vision 15B for Vision.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
GLM-5V-Turbo
$1,960
Cheapest tracked route/tier: OpenRouter
Phi-4 Reasoning Vision 15B
Unavailable
No complete token price in local provider data
Cost delta unavailable until both models have sourced input and output token prices.
Switch friction
- No overlapping tracked provider route is sourced for GLM-5V-Turbo and Phi-4 Reasoning Vision 15B; plan for SDK, billing, or endpoint changes.
- Check replacement coverage for Reasoning, Function calling, and Tool use before moving production traffic.
- No overlapping tracked provider route is sourced for Phi-4 Reasoning Vision 15B and GLM-5V-Turbo; plan for SDK, billing, or endpoint changes.
- GLM-5V-Turbo adds Reasoning, Function calling, and Tool use in local capability data.
Specs
Pricing and availability
| Pricing attribute | GLM-5V-Turbo | Phi-4 Reasoning Vision 15B |
|---|---|---|
| Input price | $1.20/1M tokens | - |
| Output price | $4/1M tokens | - |
| Providers | - |
Capabilities
| Capability | GLM-5V-Turbo | Phi-4 Reasoning Vision 15B |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | Yes | No |
| Function calling | Yes | No |
| Tool use | Yes | No |
| Structured outputs | Yes | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on reasoning mode: GLM-5V-Turbo, function calling: GLM-5V-Turbo, tool use: GLM-5V-Turbo, and structured outputs: GLM-5V-Turbo. Both models share vision and multimodal input, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
Pricing coverage is uneven: GLM-5V-Turbo has $1.20/1M input tokens and Phi-4 Reasoning Vision 15B has no token price sourced yet. Provider availability is 2 tracked routes versus 0. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose GLM-5V-Turbo when reasoning depth and broader provider choice are central to the workload. Choose Phi-4 Reasoning Vision 15B when vision-heavy evaluation are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Is GLM-5V-Turbo or Phi-4 Reasoning Vision 15B open source?
GLM-5V-Turbo is listed under MIT. Phi-4 Reasoning Vision 15B is listed under MIT. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for vision, GLM-5V-Turbo or Phi-4 Reasoning Vision 15B?
Both GLM-5V-Turbo and Phi-4 Reasoning Vision 15B expose vision. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is better for multimodal input, GLM-5V-Turbo or Phi-4 Reasoning Vision 15B?
Both GLM-5V-Turbo and Phi-4 Reasoning Vision 15B expose multimodal input. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Which is better for reasoning mode, GLM-5V-Turbo or Phi-4 Reasoning Vision 15B?
GLM-5V-Turbo has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for function calling, GLM-5V-Turbo or Phi-4 Reasoning Vision 15B?
GLM-5V-Turbo has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run GLM-5V-Turbo and Phi-4 Reasoning Vision 15B?
GLM-5V-Turbo is available on OpenRouter and Vercel AI Gateway. Phi-4 Reasoning Vision 15B is available on the tracked providers still being sourced. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-06-04. Data sourced from public model cards and provider documentation.