概要
GLM-5.1 is a 744B-parameter Mixture-of-Experts model developed by Zhipu AI (rebranded as Z.AI), released on April 7, 2026. It represents the current pinnacle of open-weight coding and agentic AI, achieving the highest public score on SWE-Bench Pro (58.4%) — edging past both GPT-5.4 and Claude Opus 4.6. The model was trained entirely on 100,000 Huawei Ascend 910B chips with zero NVIDIA hardware, making it a landmark achievement in China's AI self-sufficiency under US export controls. Z.AI completed a Hong Kong IPO in January 2026 at a ~$31.3B valuation, becoming the world's first publicly traded foundation model company.
Architecture-wise, GLM-5.1 is a post-training refinement of GLM-5, sharing the same MoE backbone (744B total, 40B active per token, 256 routed experts with top-8 selection) but with significantly enhanced coding, agentic, and long-horizon capabilities. Its defining innovation is extended autonomous execution: the model can sustain a plan-execute-analyze-optimize loop for up to 8 hours without human checkpoints, demonstrated by building a complete Linux desktop environment through 655 iterations and optimizing a vector database to 6.9× its original throughput. The model ships under the MIT license with weights available on Hugging Face.
Where GLM-5.1 falls short is general-purpose reasoning and multimodal tasks. It trails frontier closed-source models by 5-12 points on GPQA Diamond (86.2%), Humanity's Last Exam (31.0 base), and AIME 2026 (95.3%). It is text-only, with no image or audio processing. Generation speed (~40-44 tok/s) is below the competitive median, and the model's verbose output style inflates token usage. For organizations building autonomous coding agents or long-running engineering pipelines, GLM-5.1 is the strongest open-weight option available; for multimodal workflows or maximum reasoning capability, closed-source alternatives remain superior.
ベンチマーク&性能
### Comprehensive Benchmark Scores
| Category | Benchmark | GLM-5.1 | Best Competitor | Notes |
|---|---|---|---|---|
| **Coding** | SWE-Bench Pro | **58.4%** | GPT-5.4: 57.7% | #1 globally |
| **Coding** | SWE-Bench Verified | 77.8% | Claude Opus 4.6: 80.8% | 3 pts behind |
| **Coding** | NL2Repo | **42.7%** | GPT-5.4: 41.3% | #1 globally |
| **Coding** | CyberGym | **68.7%** | Claude Opus 4.6: 66.6% | #1 globally |
| **Coding** | LiveCodeBench | 52.0% | Qwen 3.5: 83.6% | Significant gap |
| **Agentic** | Terminal-Bench 2.0 | 63.5% | Claude Opus 4.6: 68.5% | 5 pts behind |
| **Agentic** | BrowseComp | 68.0% (79.3% w/ ctx mgmt) | Claude Opus 4.6: 85.9% | Context management helps |
| **Agentic** | MCP Atlas | **71.8%** | Claude Opus 4.6: 69.2% | #1 globally |
| **Agentic** | Toolathlon | 40.7% | N/A | Long-horizon weakness |
| **Math** | AIME 2026 | 95.3% | GPT-5.4: 98.7% | 3.4 pts behind |
| **Math** | HMMT Nov 2025 | 94.0% | N/A | Strong competition math |
| **Math** | HMMT Feb 2026 | 82.6% | N/A | Harder variant |
| **Math** | IMO-AnswerBench | 83.8% | N/A | Olympiad-style |
| **Reasoning** | GPQA Diamond | 86.2% | GPT-5.2: 92.4% | 6+ pts behind frontier |
| **Reasoning** | HLE (base) | 31.0% | Claude Opus 4.6: 45.0% | 14 pts behind |
| **Reasoning** | HLE (w/ tools) | 52.3% | Claude Opus 4.6: ~65%+ | Tools narrow the gap |
| **Knowledge** | BenchLM Knowledge (composite) | 83.7/100 | Rank #9 / 79 | Strong knowledge retrieval |
| **Instruction Following** | BenchLM IF (composite) | 85.9/100 | Rank #18 / 79 | Solid adherence |
### Chatbot Arena Breakdown
| Arena Category | Elo Rating | Votes |
|---|---|---|
| Text Overall | 1472 ±5.0 | 22,689 |
| Coding | 1524 ±8.4 | 6,267 |
| Hard Prompts (English) | 1504 ±7.8 | 7,539 |
| Multi-turn | 1486 ±10.4 | 3,752 |
| Longer Query | 1487 ±7.1 | 9,922 |
| Math | 1476 ±17.2 | 1,238 |
| Instruction Following | 1465 ±7.5 | 7,651 |
| Creative Writing | 1458 ±10.4 | 3,872 |
### BenchLM Category Rankings (out of 79 models)
| Category | Rank | Score |
|---|---|---|
| Mathematics | **#4** | 64.5/100 |
| Knowledge | **#9** | 83.7/100 |
| Instruction Following | #18 | 85.9/100 |
| Coding | #24 | 75.7/100 |
| Reasoning | #35 | 56.2/100 |
| Agentic | #40 | 60.6/100 |
**Overall:** Prov. #31/79 (68/100), Verified #16/35
詳細比較
### GLM-5.1 vs Claude Opus 4.6 (Anthropic)
| Dimension | GLM-5.1 | Claude Opus 4.6 |
|---|---|---|
| SWE-Bench Pro | **58.4%** | 57.3% |
| SWE-Bench Verified | 77.8% | **80.8%** |
| GPQA Diamond | 86.2% | **91.3%** |
| HLE (base) | 31.0% | **45.0%** |
| AIME 2026 | 95.3% | **98.2%** |
| Terminal-Bench 2.0 | 63.5% | **68.5%** |
| Arena Elo | ~1472 | **~1503** |
| Context Window | 200K | 200K |
| Multimodal | ❌ Text only | ✅ Vision + text |
| Input Price | **$1.40/1M** | $15/1M |
| Output Price | **$4.40/1M** | $75/1M |
| License | **MIT (open)** | Proprietary |
**Verdict:** Claude Opus 4.6 remains the overall capability leader across reasoning, multimodal tasks, and interactive agentic workflows. GLM-5.1 wins on SWE-Bench Pro, CyberGym, NL2Repo, MCP Atlas, and cost (10-17× cheaper). Choose GLM-5.1 for cost-sensitive autonomous coding agents; choose Opus 4.6 when maximum reasoning quality and multimodal support are required.
### GLM-5.1 vs GPT-5.4 (OpenAI)
| Dimension | GLM-5.1 | GPT-5.4 |
|---|---|---|
| SWE-Bench Pro | **58.4%** | 57.7% |
| AIME 2026 | 95.3% | **98.7%** |
| NL2Repo | **42.7%** | 41.3% |
| Input Price | **$1.40/1M** | ~$12/1M est. |
| Output Price | **$4.40/1M** | ~$60/1M est. |
| License | **MIT (open)** | Proprietary |
**Verdict:** GLM-5.1 narrowly leads GPT-5.4 on SWE-Bench Pro and NL2Repo at a fraction of the cost. GPT-5.4 dominates pure math reasoning (AIME 98.7% vs 95.3%). For budget-constrained coding agent deployments, GLM-5.1 offers compelling value.
### GLM-5.1 vs DeepSeek V3.2
| Dimension | GLM-5.1 | DeepSeek V3.2 |
|---|---|---|
| SWE-Bench Verified | **77.8%** | 73.1% |
| AIME 2026 | **95.3%** | 89.3% |
| CyberGym | **68.7%** | N/A |
| Input Price | $1.40/1M | **$0.27/1M** |
| Output Price | $4.40/1M | **$1.10/1M** |
| License | MIT (open) | MIT (open) |
| Autonomous Execution | **8 hrs, 1700 steps** | Not demonstrated |
**Verdict:** DeepSeek V3.2 is cheaper (3-4× on blended cost) but GLM-5.1 leads on every coding and reasoning benchmark tested. The long-horizon autonomous execution capability has no DeepSeek equivalent. Choose DeepSeek for maximum cost efficiency on simpler tasks; choose GLM-5.1 for complex autonomous coding workflows where capability matters more than per-token cost.
コミュニティ評価
The developer and research community has reacted with a mixture of genuine excitement and measured skepticism to GLM-5.1.
**Positive reception:** The SWE-Bench Pro #1 ranking generated significant attention on social media and tech forums. Multiple reviewers (Awesome Agents gave it 8.1/10, BuildFastWithAI called it the moment 'the open source is always behind narrative officially broke') praised the combination of MIT licensing, frontier-adjacent coding performance, and aggressive pricing. The long-horizon autonomous execution demos — building a Linux desktop environment in 8 hours and achieving 6.9× vector DB optimization — were widely shared as the most compelling technical demonstrations.
**Skepticism and caveats:** Several community voices noted that the SWE-Bench Pro results are self-reported by Z.AI without independent third-party verification as of July 2026. Arena.ai's independent Code Arena ranking (Elo ~1532, third place) provides partial external validation, but the exact margin over Claude and GPT-5.4 should be treated as preliminary. The 'beats Claude Opus 4.6' framing has been criticized as misleading — it holds on SWE-Bench Pro specifically but not on the broader coding composite or general reasoning tasks.
**Adoption patterns:** GLM-5.1 has seen rapid integration into the agentic development tool ecosystem, with official support for Claude Code, OpenCode, Kilo Code, Roo Code, Cline, and Droid. It is available through OpenRouter, Requesty, and WaveSpeedAI for multi-model routing setups. Z.AI's Coding Plan (Lite at ~$10/month, Pro at ~$30/month, Max at ~$80/month) has been positioned as a budget alternative to Claude Max ($100-200/month). Enterprise adoption is growing particularly among teams that require self-hosting for data sovereignty or need Chinese-language optimization.
**Geopolitical context:** The Huawei-only training story has been widely discussed as both a technical achievement and a geopolitical signal. Community consensus is that this demonstrates viable alternatives to NVIDIA infrastructure for frontier training, though questions remain about long-horizon competitiveness at scale.
ユースケース
### 1. Autonomous Coding Agents & CI/CD Pipelines
GLM-5.1's primary differentiator is its ability to sustain autonomous coding workflows for extended periods. A team can assign a complex software ticket — such as refactoring a large codebase, implementing a new feature with tests, or resolving a chain of related bugs — and let the model execute a full plan-execute-test-fix loop without human checkpoints. Example: Z.AI demonstrated the model building a complete Linux desktop environment (file browser, terminal, text editor, system monitor, games) through 655 autonomous iterations in a single session. For teams running overnight engineering pipelines where interactive latency doesn't matter, GLM-5.1's combination of SWE-Bench Pro leadership (58.4%) and 8-hour autonomous execution makes it the strongest open-weight choice. **Choose over Claude Opus 4.6** when cost per ticket is a primary constraint (10-17× cheaper) and you don't need multimodal input.
### 2. Cybersecurity Task Automation
GLM-5.1 leads CyberGym with 68.7% — nearly 20 points above the previous GLM-5 and ahead of Claude Opus 4.6 (66.6%). This benchmark evaluates 1,507 real cybersecurity tasks across simulated environments. Security teams can use GLM-5.1 for automated vulnerability scanning, exploit analysis, security patch generation, and compliance audit scripting. The MCP Atlas score (71.8%, #1) also indicates strong tool orchestration, which is critical for multi-step security workflows that chain together scanning, analysis, and remediation tools. **Choose over alternatives** when security-specific task performance and tool orchestration are priorities.
### 3. Repository Generation & Large-Scale Code Scaffolding
GLM-5.1 leads NL2Repo at 42.7%, ahead of GPT-5.4 (41.3%) and significantly above Claude Opus 4.6 (33.4%). This benchmark measures the ability to generate entire software repositories from natural language descriptions. Use cases include rapid prototyping of new projects, generating boilerplate for microservice architectures, and creating scaffolded codebases for hackathons or proof-of-concepts. Combined with the 200K context window and 128K max output, GLM-5.1 can produce substantial multi-file artifacts in a single session. **Choose over alternatives** when the goal is generating complete, multi-file codebases from specifications rather than incremental edits.
### 4. Mathematical & Scientific Computing
GLM-5.1 ranks #4 overall in BenchLM's mathematics category (AIME 2026: 95.3%, HMMT Nov 2025: 94.0%, IMO-AnswerBench: 83.8%) and performs well on knowledge retrieval (#9, 83.7/100). It is suitable for computational mathematics, algorithm development, quantitative analysis, and STEM education applications. While it trails GPT-5.4 (AIME 98.7%) and Claude Opus 4.6 (AIME 98.2%) on pure math competition problems, it remains highly competitive at a fraction of the cost. **Choose over cheaper alternatives like DeepSeek V3.2** (AIME 89.3%) when mathematical accuracy is critical; **choose over frontier closed models** when budget constraints apply and the 3-4 point gap is acceptable.
最新ニュース
- **April 7, 2026:** GLM-5.1 weights released on Hugging Face under MIT license (`zai-org/GLM-5.1`). Achieved #1 on SWE-Bench Pro (58.4%). Trained on 100,000 Huawei Ascend 910B chips with zero NVIDIA hardware.
- **March 27, 2026:** GLM-5.1 API made available on Z.AI's platform. Pricing set at $1.40/$4.40 per 1M tokens with cache reads at $0.26/1M.
- **March 2026:** Z.AI launched GLM Coding Plan subscriptions: Lite (~$10/month), Pro (~$30/month), Max (~$80/month) tiers.
- **January 8, 2026:** Z.AI (formerly Zhipu AI) completed Hong Kong IPO raising ~HKD 4.35B (~$558M USD) at ~$31.3B valuation — first publicly traded foundation model company.
- **April 2026:** Peak-hour pricing adjustment: usage billed at 3× standard rate during 14:00-18:00 Beijing Time. Off-peak promotion available through April 2026 at 1× rate.
- **Deployment updates:** SGLang v0.5.10+ and vLLM v0.19.0+ now support GLM-5.1 with FP8 quantization. Lambda Cloud offers single-node deployment on NVIDIA HGX B200 with ~1,345 tok/s generation throughput.
- **Sibling models in the GLM-5 family:** GLM-5 (Feb 2026), GLM-5-Turbo (March 2026), GLM-5V-Turbo, GLM-5 (Reasoning), and GLM-5.2 (prov. score 81 on BenchLM — significantly ahead of GLM-5.1's 68).