GLM-5 vs Mixtral 8x7B
GLM-5 (2026) and Mixtral 8x7B (2023) are frontier reasoning models from Zhipu AI and MistralAI. GLM-5 ships a 200k-token context window, while Mixtral 8x7B ships a 32k-token context window. On Google-Proof Q&A, GLM-5 leads by 31.2 pts. On pricing, Mixtral 8x7B costs $0.15/1M 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.
Mixtral 8x7B is ~300% cheaper at $0.15/1M; pay for GLM-5 only for reasoning depth.
Decision scorecard
Local evidence first| Signal | GLM-5 | Mixtral 8x7B |
|---|---|---|
| Best for | reasoning-heavy apps, tool-calling agents, and provider-routed production | provider-routed production |
| Decision fit | Coding, RAG, and Agents | Coding and Classification |
| Context window | 200k | 32k |
| Cheapest output | $2.08/1M tokens | $0.45/1M tokens |
| Provider routes | 7 tracked | 18 tracked |
| Shared benchmarks | Google-Proof Q&A leader | 1 rows |
Decision tradeoffs
- GLM-5 holds a shared-benchmark lead on Google-Proof Q&A, ahead by 31.2 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.
- Mixtral 8x7B has the lower cheapest tracked output price at $0.45/1M tokens.
- Mixtral 8x7B has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Mixtral 8x7B for Coding and Classification.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
GLM-5
$1,000
Cheapest tracked route/tier: OpenRouter
Mixtral 8x7B
$233
Cheapest tracked route/tier: Mistral AI Studio
Estimated monthly gap: $768. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on NVIDIA NIM, GCP Vertex AI, and Fireworks AI; start route-level A/B tests there.
- Mixtral 8x7B is $1.63/1M 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.
- Provider overlap exists on Fireworks AI, GCP Vertex AI, and NVIDIA NIM; start route-level A/B tests there.
- GLM-5 is $1.63/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- GLM-5 adds Reasoning, Function calling, and Tool use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-02-11 | 2023-12-11 |
| Context window | 200k | 32k |
| Parameters | 744B total, 40B active | 8x7B |
| Architecture | mixture of experts | mixture of experts |
| License | MIT(OSI) | Apache 2.0(OSI) |
| Openness | Open source | Open source |
| Commercial use | Commercial use allowed | Commercial use allowed |
| Knowledge cutoff | 2025-11 | 2023-12 |
Pricing and availability
| Pricing attribute | GLM-5 | Mixtral 8x7B |
|---|---|---|
| Input price | $0.60/1M tokens | $0.15/1M tokens |
| Output price | $2.08/1M tokens | $0.45/1M tokens |
| Providers |
Capabilities
| Capability | GLM-5 | Mixtral 8x7B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| 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
| Benchmark | GLM-5 | Mixtral 8x7B |
|---|---|---|
| Google-Proof Q&A | 86.0 | 54.8 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has GLM-5 at 86 and Mixtral 8x7B at 54.8, with GLM-5 ahead by 31.2 points. The largest visible gap is 31.2 points on Google-Proof Q&A, 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 structured outputs: GLM-5. Both models share the core language-model surface, 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, GLM-5 lists $0.60/1M input and $2.08/1M output tokens on the cheapest tracked provider, while Mixtral 8x7B lists $0.15/1M input and $0.45/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Mixtral 8x7B lower by about $0.80 per million blended tokens. Availability is 7 providers versus 18, so concentration risk also matters.
Choose GLM-5 when reasoning depth and larger context windows are central to the workload. Choose Mixtral 8x7B when provider fit, lower input-token cost, and broader provider choice 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, GLM-5 or Mixtral 8x7B?
GLM-5 supports 200k tokens, while Mixtral 8x7B supports 32k 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, GLM-5 or Mixtral 8x7B?
Mixtral 8x7B is cheaper on tracked token pricing. GLM-5 costs $0.60/1M input and $2.08/1M output tokens. Mixtral 8x7B costs $0.15/1M input and $0.45/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is GLM-5 or Mixtral 8x7B open source?
GLM-5 is listed under MIT. Mixtral 8x7B is listed under Apache 2.0. 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, GLM-5 or Mixtral 8x7B?
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, GLM-5 or Mixtral 8x7B?
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 GLM-5 and Mixtral 8x7B?
GLM-5 is available on Fireworks AI, OpenRouter, Together AI, GCP Vertex AI, and NVIDIA NIM. Mixtral 8x7B is available on Databricks Foundation Model Serving, NVIDIA NIM, GCP Vertex AI, AWS Bedrock, and OctoAI API (Deprecated). 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.