LLM Reference

GLM-5

Released
2026-02-11
Last refreshed
2026-06-30
Status
Researched 64d ago
Open sourceCommercial use: permittedCodingRAGAgentsLong contextClassificationJSON / Tool use

GLM-5 is worth evaluating for coding, rag, and agents when its provider route and context window match the workload.

Use it for

  • Teams evaluating coding, rag, and agents
  • Workloads that can use a 200k context window
  • Buyers comparing 4 tracked provider routes

Do not use it for

  • Vision or document-understanding workloads
Specifications
Family
GLM-5
Released
2026-02-11
Context
200k
Max output
128,000
Parameters
744B total, 40B active
Architecture
Mixture of Experts
Knowledge cutoff
2025-11
Specialization
general
Openness
Open source
License
MITOSI-approvedCommercial use: permitted
Weights
Available
Code
Unknown
Training
Fine-tuned
Created by

Chinese AI research lab developing GLM language models.

Beijing, China
Founded 2019
Website
Pricing
Output / 1M
$2.08
Input / 1M
$0.600

Cheapest of 7 routes · OpenRouter

About

Flagship open-weight foundation model from Zhipu AI with 744B parameters (40B active per token) in Mixture of Experts architecture. Trained on 28.5T tokens using DeepSeek Sparse Attention on Huawei Ascend hardware. Achieves state-of-the-art performance on coding and agentic benchmarks (SWE-bench Verified: 77.8%). Supports autonomous planning, multi-step tool use, and self-correction.

Top use-case fit: coding, agents, and build tasks

Coding

Q/$ C

3 relevant benchmarks in the decision map.

RAG

Included by capability and metadata signals in the decision map.

Agents

Q/$ C

2 relevant benchmarks in the decision map.

Provider price ladder

Compare all 7

Compare API pricing across 4 providers for input and output tokens, batch, and cached reads when available.

ProviderInput / 1MOutput / 1MRoute
OpenRouter$0.600$2.08
Serverless
Fireworks AI$1.00$3.20
Serverless
GCP Vertex AI$1.00$3.20
Serverless
Novita AI$1.00$3.20
Serverless

Available via routers & gateways(14)

Capabilities

ReasoningJSON / Tool useStructured OutputsPrompt Caching

Benchmark peer barsfor Coding

Benchmark scores(9)

Scores are benchmark-specific and are direction-aware: the same numeric gap can mean very different outcomes across suites. Use the leaderboard context and this model's provider route to decide whether the winning margin is meaningful for your workload.
BenchmarkScoreVersionEvaluationSource
SWE-bench Pro55.1Observed 2026-03-01Source
SWE-bench Verified77.8SWE-bench VerifiedObserved 2026-04-24Source
τ-bench82.1τ-benchObserved 2026-04-24Source
SWE-rebench62.8pass@1 (best of 5 runs)Observed 2026-05-28Source
AIME 202592.7AIME 2026 (accuracy)Observed 2026-06-07Source
Google-Proof Q&A86.0GPQA Diamond (accuracy)Observed 2026-06-07Source
Humanity's Last Exam30.5HLE text-only (accuracy)Observed 2026-06-07Source
LiveCodeBench81.9LiveCodeBench v6 (accuracy)Observed 2026-06-07Source
MMLU PRO86.0From model card / Apex Agents listing (accuracy)Observed 2026-06-07Source

Migration checks

No linked migration route is available for this model yet.

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