GLM 5.2 vs Mistral Large 2: Benchmarks, Cost, and Enterprise Fit

You are evaluating two frontier models for a production deployment: GLM 5.2, built by THUDM and served through Z.ai, and Mistral Large 2, Mistral AI's flagship model from France. Both target serious enterprise use, but the differences in price, speed, context capacity, and licensing are large enough to change architectural decisions.

The short version. GLM 5.2 scores 51 on the Artificial Analysis Intelligence Index; Mistral Large 2 scores ~47. GLM 5.2 generates tokens at 158 t/s versus ~60 t/s for Mistral Large 2 — nearly three times faster. Output tokens cost $4.40/M on GLM 5.2 versus $6.00/M on Mistral Large 2, a 27% savings. The context window gap is even starker: GLM 5.2 supports 1,048,576 tokens (~1M) while Mistral Large 2 caps at 128K, an 8x difference. GLM 5.2 ships under the MIT license with no commercial restrictions; Mistral Large 2 uses a proprietary research license that limits redistribution and commercial self-hosting. The one area where Mistral Large 2 leads is HumanEval (~92% versus GLM 5.2's 90%+) and European hosting options for GDPR-sensitive workloads.

Choose GLM 5.2 if you process long documents, codebases, or transcripts beyond 128K tokens; if cost efficiency at scale matters; or if you need truly open weights for fine-tuning or on-premises deployment without license restrictions.

Choose Mistral Large 2 if European data residency is a hard compliance requirement, you already operate on Azure or Google Cloud where Mistral is available as a managed service, or your workflow requires image input alongside text.

Quick Comparison

Benchmark Performance

GLM 5.2 was released with a comprehensive public evaluation suite. Mistral Large 2's published benchmarks focus primarily on MMLU and HumanEval, so direct benchmark-to-benchmark comparison is incomplete on both sides.

GLM 5.2's GPQA Diamond score of 89% is a standout result. GPQA Diamond is a graduate-level science reasoning benchmark intentionally designed to be difficult even for domain experts with access to external resources, making it one of the harder evaluation tools in widespread use. A score in the high eighties places GLM 5.2 among the top performers on expert-level reasoning.

For software engineering, GLM 5.2's 62.1% on SWE-bench Pro reflects real agentic coding ability — multi-file edits, bug diagnosis from issue descriptions, and end-to-end test execution inside a full repository environment. Terminal-Bench v2.1 at 78% measures performance in live command-line environments rather than isolated coding puzzles. These benchmarks are closer to production conditions than HumanEval, which tests isolated Python function completion.

HumanEval is the one area where Mistral Large 2 holds an edge: ~92% versus GLM 5.2's 90%+. The gap is approximately 2 percentage points, which is within measurement noise for models at this capability level, and does not indicate a systematic advantage in production code generation. Mistral AI has not published SWE-bench Pro or Terminal-Bench results for Mistral Large 2, so comparisons on those dimensions cannot be made.

Pricing Breakdown

Pricing differences compound at production volumes. GLM 5.2's output token rate of $4.40/M saves 27% compared to Mistral Large 2's $6.00/M. Input tokens are similarly cheaper: $1.40/M versus $2.00/M, a 30% reduction.

GLM 5.2 also offers prompt caching at $0.26 per million cached input tokens. For workloads with large, repeated system prompts — document QA over a fixed corpus, RAG pipelines with persistent tool description blocks, or agents with lengthy instructions — caching can reduce effective input costs to a fraction of the standard rate.

For a team running 50M output tokens per month, the difference between GLM 5.2 and Mistral Large 2 is approximately $80,000 per year. At 200M output tokens per month, that gap reaches $320,000 annually. At those volumes, model selection is a material budget decision, not a technical footnote.

See also: GLM 5.2 pricing breakdown for a deeper look at caching, batch tiers, and volume scenarios.

Speed and Latency

Inference speed affects interactive applications — chatbots, coding copilots, real-time document analysis — where latency is directly visible to users.

GLM 5.2 runs at 158 tokens per second, which Artificial Analysis ranks as the third fastest among frontier-class models currently available. At that speed, a 1,000-token response completes in roughly 6 seconds of generation time. Mistral Large 2 at ~60 t/s would take approximately 17 seconds for the same output — nearly three times as long. In a streaming interface, that difference is immediately perceptible.

The TTFT of 1.54 seconds for GLM 5.2 means the first token arrives in under two seconds, which falls within the threshold most users experience as responsive. Mistral AI does not publish a comparable TTFT figure for Mistral Large 2's managed API.

Speed advantages also matter in pipeline throughput. Faster token generation means shorter queue times under concurrent load, more requests handled per unit of compute, and faster iteration in development and testing cycles.

Context Window

The context window gap between these two models is the most consequential differentiator for enterprise document workflows. GLM 5.2 supports 1,048,576 tokens. Mistral Large 2 caps at 128,000 tokens — one eighth the capacity.

One million tokens corresponds to approximately:

• 750,000 words, or roughly ten full-length novels
• A 40,000-line codebase loaded in its entirety in a single prompt
• 60 to 80 hours of meeting transcripts
• Several hundred pages of legal contracts, regulatory filings, or financial reports

At 128K tokens, Mistral Large 2 handles one-eighth of these volumes before context limits are reached. For most conversational and short-document use cases, 128K is sufficient. For enterprise workflows involving large codebases, comprehensive legal documents, full research corpora, or accumulated multi-session context, the limit becomes a real constraint.

Retrieval-augmented generation (RAG) is the standard workaround for short-context models: chunk the document, embed the chunks, retrieve the top-k at query time, and assemble a prompt from the retrieved pieces. RAG works, but it introduces retrieval errors, increases latency, requires embedding infrastructure, and makes results sensitive to chunking strategy and retrieval quality. With a 1M token window, many of those RAG pipelines collapse back into a single direct model call — simpler, faster, and less likely to miss relevant content due to retrieval gaps.

License and Self-Hosting

The licensing difference between these two models has direct legal and operational consequences for any enterprise that intends to fine-tune, self-host, or redistribute.

GLM 5.2 is released under the MIT license. Weights are available on Hugging Face at THUDM/GLM-5.2. MIT imposes no restrictions on commercial use, modification, redistribution, or fine-tuning. You can run it on your own hardware, incorporate it into a commercial product, publish derived models, or distribute it internally without notifying THUDM or paying royalties. There are no use-case restrictions.

Mistral Large 2 is distributed under the Mistral Research License. Weights are downloadable, which makes the model feel semi-open, but the license includes commercial restrictions. Running Mistral Large 2 in a commercial product at scale may require checking license scope or negotiating a commercial agreement with Mistral AI. This is a meaningfully different situation from MIT and is a relevant legal consideration for any team building a product on top of the model weights.

For teams that need to fine-tune and self-host for security, data privacy, or cost reasons: GLM 5.2 is the clearer choice on licensing terms. For teams accessing either model purely through a managed API, the licensing difference matters less in day-to-day operations — both providers offer standard API terms of service.

Modalities. GLM 5.2 is text-only — it does not accept image, audio, or video input at this time. Mistral Large 2 supports limited image input through La Plateforme. If your workflow involves vision tasks such as document OCR with layout understanding, chart reading, or screenshot analysis, Mistral Large 2 offers a capability that GLM 5.2 currently does not provide.

When to Choose GLM 5.2

• Your documents, codebases, or conversation histories exceed 128K tokens and you want to avoid chunking or RAG overhead
• Output volume is high and the 27% savings at $4.40/M output versus $6.00/M matters at scale
• You need MIT-licensed open weights for fine-tuning, internal redistribution, or on-premises deployment without license negotiation
• Inference speed is important for interactive or real-time applications — 158 t/s versus ~60 t/s is a visible difference in streaming UIs
• Agentic coding is a primary use case and SWE-bench Pro and Terminal-Bench results are your benchmark of choice
• You want prompt caching to reduce costs on workloads with repeated system prompt content
• You prefer access through Z.ai or OpenRouter without cloud-marketplace lock-in

When to Choose Mistral Large 2

• European data residency (GDPR, Schrems II, sector-specific regulation) is a non-negotiable compliance requirement and French or EU hosting is preferred
• You already have contracts with Microsoft Azure or Google Cloud and want a managed Mistral integration through their existing marketplace
• Your workflow requires image input alongside text and you need a single model for both modalities
• You have an established Mistral enterprise support agreement and relationship
• Your use case comfortably fits within the 128K context window with no plans to expand

Frequently Asked Questions

Is GLM 5.2 actually three times faster than Mistral Large 2?

According to Artificial Analysis benchmarks, GLM 5.2 delivers 158 tokens per second, placing it third among frontier-class models by throughput. Mistral Large 2 operates at approximately 60 tokens per second by the same methodology. The ratio is roughly 2.6x, which rounds to nearly three times faster. For a 1,000-token streaming response, GLM 5.2 finishes in about 6 seconds; Mistral Large 2 takes about 17 seconds. The difference is user-perceptible in any streaming interface.

Does the context window difference matter for typical applications?

For standard chat, summarization over short documents, and typical RAG queries where retrieved context fits in a few thousand tokens, 128K is more than sufficient and the gap is irrelevant. The 1M window becomes important for enterprise-scale document processing: loading a full codebase into a single prompt, reviewing a book-length contract without chunking, accumulating months of conversation history in a persistent agent, or ingesting a complete year of support tickets for pattern analysis. The larger the document or corpus, the more the 8x context advantage changes what is architecturally possible without adding retrieval infrastructure.

Is Mistral Large 2 truly open source?

No. Mistral Large 2 is distributed under the Mistral Research License, which permits downloads and certain non-commercial uses but includes restrictions on commercial redistribution and large-scale commercial deployment. GLM 5.2 uses the MIT license, which places no restrictions on commercial use, modification, or redistribution. The difference is significant for any team planning to fine-tune the model or incorporate the weights into a product that is distributed to customers.

Which model is better for coding tasks?

Both are competitive on coding. GLM 5.2 scores 90%+ on HumanEval and 62.1% on SWE-bench Pro; Mistral Large 2 scores ~92% on HumanEval. For isolated function completion, Mistral Large 2 has a marginal lead of roughly 2 percentage points. For real-world agentic coding — fixing a bug described in a GitHub issue, making multi-file edits, and running the test suite inside a real repository — GLM 5.2's SWE-bench Pro result is the more applicable benchmark, and Mistral AI has not published a comparable SWE-bench Pro result for Mistral Large 2.

How do I access GLM 5.2 via API?

GLM 5.2 is available through Z.ai at base_url: https://api.z.ai/v1 with model identifier glm-5.2. It is also accessible through OpenRouter using the identifier z-ai/glm-5.2. Both endpoints are compatible with the OpenAI Python and JavaScript SDK clients — changing the base URL and model name is typically all that is required. See the GLM 5.2 API guide for full setup instructions and code examples.

What is the annual cost difference at scale?

At 50M output tokens per month: GLM 5.2 costs $220/M × 50 = $220 per month on output, versus Mistral Large 2 at $300 per month — a difference of $80/month or $960/year. At 50M tokens that sounds modest, but at 200M output tokens per month the difference is $320/month or $3,840/year just on output tokens, before input costs. Large-scale deployments with billions of tokens per month see this multiply into six-figure annual savings.

Can I run GLM 5.2 without an API account?

Yes. glm5.app/chat provides a browser interface that requires no API key, no account, and no code. It is a straightforward way to test GLM 5.2 on real prompts before committing to an API integration.

Bottom Line

GLM 5.2 outscores Mistral Large 2 on the Artificial Analysis Intelligence Index (51 vs ~47), costs 27% less on output tokens ($4.40 vs $6.00/M), runs nearly three times faster (158 vs ~60 t/s), and offers an 8x larger context window (1M vs 128K) — all under an MIT license with no commercial restrictions. Mistral Large 2 holds a marginal HumanEval edge, European hosting through Mistral's own infrastructure and cloud marketplaces, limited image input capability, and established enterprise support relationships. For most enterprise AI workloads where cost, speed, context depth, and licensing flexibility are the primary criteria, GLM 5.2 is the stronger choice. Mistral Large 2 earns its place specifically when European data residency is a hard compliance requirement or when existing Azure and GCP contracts make marketplace-native access the path of least resistance.

Try GLM 5.2 — no API key needed: glm5.app/chat.

Sources

• Artificial Analysis — GLM 5.2 Model Page
• GLM 5.2 weights on Hugging Face
• Artificial Analysis — Mistral Large 2 Model Page
• Mistral AI — Models Overview Documentation
• Z.ai API Reference