DeepSeek V3.2 vs GLM-5
DeepSeek V3.2 (2025) and GLM-5 (2026) are frontier reasoning models from DeepSeek and Zhipu AI. DeepSeek V3.2 ships a 160k-token context window, while GLM-5 ships a 200k-token context window. On SWE-bench Verified, GLM-5 leads by 7.8 pts. On pricing, DeepSeek V3.2 costs $0.25/1M input tokens versus $0.60/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
DeepSeek V3.2 is ~138% cheaper at $0.25/1M; pay for GLM-5 only for reasoning depth.
Decision scorecard
Local evidence first| Signal | DeepSeek V3.2 | GLM-5 |
|---|---|---|
| Best for | provider-routed production | reasoning-heavy apps, tool-calling agents, and provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 160k | 200k |
| Cheapest output | $0.38/1M tokens | $2.08/1M tokens |
| Provider routes | 7 tracked | 7 tracked |
| Shared benchmarks | 3 rows | SWE-bench Verified leader |
Decision tradeoffs
- DeepSeek V3.2 has the lower cheapest tracked output price at $0.38/1M tokens.
- DeepSeek V3.2 uniquely exposes Code execution in local model data.
- Local decision data tags DeepSeek V3.2 for Coding, RAG, and Agents.
- GLM-5 holds a shared-benchmark lead on SWE-bench Verified, ahead by 7.8 points.
- GLM-5 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- GLM-5 uniquely exposes Reasoning, Function calling, and Tool use in local model data.
- Local decision data tags GLM-5 for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
DeepSeek V3.2
$296
Cheapest tracked route/tier: OpenRouter
GLM-5
$1,000
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $704. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Fireworks AI, OpenRouter, and NVIDIA NIM; start route-level A/B tests there.
- GLM-5 is $1.70/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Code execution before moving production traffic.
- GLM-5 adds Reasoning, Function calling, and Tool use in local capability data.
- Provider overlap exists on Fireworks AI, NVIDIA NIM, and OpenRouter; start route-level A/B tests there.
- DeepSeek V3.2 is $1.70/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning, Function calling, and Tool use before moving production traffic.
- DeepSeek V3.2 adds Code execution in local capability data.
Specs
Pricing and availability
| Pricing attribute | DeepSeek V3.2 | GLM-5 |
|---|---|---|
| Input price | $0.25/1M tokens | $0.60/1M tokens |
| Output price | $0.38/1M tokens | $2.08/1M tokens |
| Providers |
Capabilities
| Capability | DeepSeek V3.2 | GLM-5 |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | Yes |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | Yes | Yes |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | DeepSeek V3.2 | GLM-5 |
|---|---|---|
| SWE-bench Verified | 70.0 | 77.8 |
| Google-Proof Q&A | 84.0 | 86.0 |
| SWE-rebench | 60.9 | 62.8 |
Deep dive
On shared benchmark coverage, SWE-bench Verified has DeepSeek V3.2 at 70 and GLM-5 at 77.8, with GLM-5 ahead by 7.8 points; Google-Proof Q&A has DeepSeek V3.2 at 84 and GLM-5 at 86, with GLM-5 ahead by 2 points; SWE-rebench has DeepSeek V3.2 at 60.9 and GLM-5 at 62.8, with GLM-5 ahead by 1.9 points. The largest visible gap is 7.8 points on SWE-bench Verified, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on reasoning mode: GLM-5, function calling: GLM-5, tool use: GLM-5, and code execution: DeepSeek V3.2. Both models share structured outputs, 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.
For cost, DeepSeek V3.2 lists $0.25/1M input and $0.38/1M output tokens on the cheapest tracked provider, while GLM-5 lists $0.60/1M input and $2.08/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts DeepSeek V3.2 lower by about $0.75 per million blended tokens. Availability is 7 providers versus 7, so concentration risk also matters.
Choose DeepSeek V3.2 when coding workflow support and lower input-token cost are central to the workload. Choose GLM-5 when reasoning depth and larger context windows are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, DeepSeek V3.2 or GLM-5?
GLM-5 supports 200k tokens, while DeepSeek V3.2 supports 160k tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is cheaper, DeepSeek V3.2 or GLM-5?
DeepSeek V3.2 is cheaper on tracked token pricing. DeepSeek V3.2 costs $0.25/1M input and $0.38/1M output tokens. GLM-5 costs $0.60/1M input and $2.08/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek V3.2 or GLM-5 open source?
DeepSeek V3.2 is listed under MIT. GLM-5 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 reasoning mode, DeepSeek V3.2 or GLM-5?
GLM-5 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, DeepSeek V3.2 or GLM-5?
GLM-5 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 DeepSeek V3.2 and GLM-5?
DeepSeek V3.2 is available on Fireworks AI, NVIDIA NIM, AWS Bedrock, OpenRouter, and Microsoft Foundry. GLM-5 is available on Fireworks AI, OpenRouter, Together AI, GCP Vertex AI, and NVIDIA NIM. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-05-22. Data sourced from public model cards and provider documentation.