Two frontier models launched within a week of each other in February 2026. GLM-5 from Zhipu AI: 744B parameters, trained on Huawei chips, open-sourced under MIT. Claude Opus 4.6 from Anthropic: the new #1 on Chatbot Arena, with 1M token context in beta.
The SWE-bench gap between them is just 3 percentage points. The price gap is 5x. Whether that tradeoff makes sense depends entirely on what you're building.
TL;DR: Quick Verdict
- Best for coding accuracy: Claude Opus 4.6. Leads SWE-bench (80.8% vs 77.8%), Terminal-Bench (65.4% vs 56.2%), and parallelizes file operations faster.
- Best for cost-efficiency: GLM-5. At $1/$3.20 per MTok, a one-hour agent session costs ~$0.09 vs ~$8.00 on Opus. Handles 70-80% of workloads at acceptable quality.
- Best for self-hosting: GLM-5. MIT license, 40B active parameters (MoE), available on Hugging Face. Opus 4.6 is API-only.
- Best for deep reasoning: Claude Opus 4.6. #1 on Chatbot Arena (1506 Elo), leads GDPval-AA by 144 Elo over GPT-5.2, and scores 76% on MRCR v2 at 1M context.
- Best for browsing/agentic tasks: GLM-5. Scores 75.9% on BrowseComp (with context management) vs Opus 4.5's 37%, and leads open-source models on MCP-Atlas.
Morph angle: Both models generate code diffs that need to be applied to files. Morph Fast Apply processes those diffs at 10,500+ tok/sec with 98% first-pass accuracy, regardless of which model generated them.
Head-to-Head Comparison
| Specification | GLM-5 | Claude Opus 4.6 |
|---|---|---|
| Developer | Zhipu AI | Anthropic |
| Release Date | February 11, 2026 | February 5, 2026 |
| Parameters | 744B total / 40B active (MoE) | Undisclosed |
| Training Data | 28.5T tokens | Undisclosed |
| Training Hardware | 100K Huawei Ascend 910B | Undisclosed (NVIDIA) |
| Context Window | 200K tokens | 200K standard / 1M beta |
| Max Output | 128K tokens | 128K tokens |
| Input Price | $1.00/MTok | $5.00/MTok |
| Output Price | $3.20/MTok | $25.00/MTok |
| License | MIT (open source) | Proprietary (API only) |
| Chatbot Arena Elo | 1451 | 1506 |
| SWE-bench Verified | 77.8% | 80.8% |
Benchmark Breakdown
Both models compete at the frontier, but they win in different categories. Opus 4.6 dominates reasoning-heavy evaluations. GLM-5 punches above its weight on browsing and agentic tool use.
Reasoning and Knowledge
| Benchmark | GLM-5 | Claude Opus 4.6 | Winner |
|---|---|---|---|
| MMLU | ~92% | 91% | GLM-5 (marginal) |
| GPQA Diamond | 86.0% | 91% | Opus 4.6 |
| Humanity's Last Exam | 30.5 (50.4 w/ tools) | 53% | Opus 4.6 |
| AIME 2025 | 92.7% (2026 I) | 100% | Opus 4.6 |
| MATH | Not reported | 93% | Opus 4.6 |
| SimpleQA | Not reported | 72% | Opus 4.6 |
Opus 4.6 sweeps reasoning benchmarks. The GPQA Diamond gap (86% vs 91%) reflects a real difference in scientific reasoning ability. On Humanity's Last Exam (the hardest public evaluation), Opus scores 53% vs GLM-5's 30.5% without tools, though GLM-5 narrows significantly to 50.4% with tool augmentation.
Agentic and Browsing
| Benchmark | GLM-5 | Claude Opus 4.6 | Winner |
|---|---|---|---|
| BrowseComp | 62.0% (75.9% w/ ctx mgmt) | ~37% (Opus 4.5 baseline) | GLM-5 |
| MCP-Atlas | 67.8% | Not reported | GLM-5 |
| τ²-Bench | 89.7% | 91.6% (Opus 4.5) | Close (Opus edge) |
| Vending Bench 2 | $4,432 | $4,967 (Opus 4.5) | Opus (higher = better) |
GLM-5's BrowseComp score (75.9% with context management) is genuinely impressive. It more than doubles Opus 4.5's score on this web browsing benchmark, suggesting strong tool-use and information retrieval capabilities. Anthropic hasn't published Opus 4.6 BrowseComp numbers yet, but the official blog claims it "performs better than any other model on BrowseComp."
On the Chatbot Arena leaderboard (LMSYS), Opus 4.6 Thinking sits at #1 overall with a 1506 Elo. GLM-5 holds the top open-source position at 1451. The top three open-source models (GLM-5, Kimi K2.5 at 1447, GLM-4.7 at 1445) cluster within 6 points of each other, but Opus 4.6 maintains a clear lead above all of them.
Coding Performance
| Benchmark | GLM-5 | Claude Opus 4.6 | Winner |
|---|---|---|---|
| SWE-bench Verified | 77.8% | 80.8% | Opus 4.6 |
| SWE-bench Multilingual | 73.3% | Not reported | GLM-5 (only reported) |
| Terminal-Bench 2.0 | 56.2% | 65.4% | Opus 4.6 |
| HumanEval | ~90-99% | 95% | Close |
| LiveCodeBench | Not reported | 76% | Opus 4.6 |
| CyberGym | 43.2% | 50.6% (Opus 4.5) | Opus |
The SWE-bench Verified gap is 3 points (77.8% vs 80.8%). On paper, that looks close. In practice, the difference is larger than it appears. SWE-bench tasks at the 78-81% range are the hardest ones: multi-file changes, subtle type errors, and edge-case handling. Scoring 3 points higher in this range means solving meaningfully harder problems.
Terminal-Bench 2.0 widens the gap further. At 65.4% vs 56.2%, Opus 4.6 completes agentic terminal tasks about 16% more often. This benchmark tests the full agent loop: reading files, running commands, iterating on errors, and applying fixes.
Real-World Coding Speed
Developer reports consistently note that Opus 4.6 finishes multi-file coding tasks roughly twice as fast as GLM-5. The reason isn't raw token speed (GLM-5 outputs at 71 tok/sec vs Opus at similar speeds). The difference is execution strategy. Opus fires off parallel file reads, runs lint and typecheck simultaneously, and batches operations together. GLM-5 tends to execute sequentially, thinking longer before each action.
Workaround: splitting tasks into a planning phase and implementation phase brings GLM-5's effective speed much closer to Opus. But that requires restructuring your prompts.
The 3-Point SWE-bench Gap in Context
GLM-5 at 77.8% still outperforms every model released before Q4 2025. It trails Opus 4.6 (80.8%) and GPT-5.2 (80.0%) but beats Gemini 3.0 Pro on this benchmark. For most everyday coding tasks, the quality difference is negligible. The gap surfaces on complex, multi-file refactors requiring deep context understanding.
API Pricing Comparison
This is where the comparison gets interesting. GLM-5 doesn't just cost less. It costs dramatically less.
| Metric | GLM-5 | Claude Opus 4.6 |
|---|---|---|
| Input (per 1M tokens) | $1.00 | $5.00 |
| Output (per 1M tokens) | $3.20 | $25.00 |
| Blended (3:1 ratio) | $1.55/MTok | $10.00/MTok |
| Input (200K+ context) | $1.00 (no change) | $10.00 |
| Output (200K+ context) | $3.20 (no change) | $37.50 |
Real-World Cost Examples
For a sustained one-hour agent session (approximately 600K input + 200K output tokens), GLM-5 costs roughly $0.09. The same session on Opus 4.6 costs approximately $8.00. That's not a rounding error. Over a month of heavy development (40+ hours of agent usage), GLM-5 costs ~$3.60 vs ~$320 for Opus.
Third-party providers push GLM-5 costs even lower. DeepInfra offers GLM-5 FP8 at $1.24/MTok blended. Novita and SiliconFlow offer it at $1.55/MTok. Since GLM-5 is MIT-licensed, anyone can host it, and competition drives prices down.
Opus 4.6 has no competitive hosting market. Anthropic is the sole provider. AWS Bedrock and Google Vertex AI offer it at approximately the same rates. Prompt caching can reduce costs on repeated queries, but the base pricing is fixed.
The Cost-per-Outcome Argument
Raw token pricing doesn't tell the full story. If Opus 4.6 solves a task on the first attempt that takes GLM-5 three retries, Opus is cheaper per outcome. For complex coding tasks, developers report Opus's first-pass success rate is noticeably higher. For straightforward tasks (boilerplate, simple refactors, documentation), GLM-5's quality is sufficient and the cost savings compound.
Context Window and Output
| Feature | GLM-5 | Claude Opus 4.6 |
|---|---|---|
| Standard Context | 200K tokens | 200K tokens |
| Extended Context | None | 1M tokens (beta) |
| Max Output | 128K tokens | 128K tokens |
| Long-Context Accuracy | Not benchmarked | 76% (MRCR v2, 8-needle, 1M) |
| Context Caching | Supported | Supported (with pricing premium) |
| Structured Output | Supported | Supported |
At the standard 200K tier, they're equivalent. Both support 128K output tokens, context caching, and structured output with function calling.
Opus 4.6's 1M token beta context is the differentiator. Anthropic reports 76% accuracy on MRCR v2 (8-needle retrieval at 1M context), compared to Sonnet 4.5's 18.5%. If your use case involves processing entire codebases, long legal documents, or multi-hundred-page analyses in a single pass, Opus 4.6 has no open-source competitor at this context length.
GLM-5 at 200K is still generous. Most coding agent interactions use 10-50K tokens. The 200K ceiling rarely becomes a bottleneck unless you're ingesting very large repositories or lengthy documents.
Architecture and Training
GLM-5: The Open-Source Frontier
GLM-5 uses a mixture-of-experts (MoE) architecture: 744B total parameters with 40B active per inference pass. Training ran on 28.5 trillion tokens across 100,000 Huawei Ascend 910B processors. Not a single NVIDIA chip was involved. Zhipu AI used DeepSeek Sparse Attention (DSA) and their proprietary "Slime" asynchronous reinforcement learning system.
The geopolitical angle matters practically. After the U.S. Commerce Department added Zhipu AI to the Entity List in January 2025, the company retrained all flagship models on domestic Chinese hardware. The fact that GLM-5 matches frontier performance without access to NVIDIA's latest chips is a significant engineering achievement, regardless of where you stand on the politics.
Zhipu went public on the Hong Kong Stock Exchange in January 2026 at a $6.5B valuation. By mid-February, market cap had surged past $40B. The MIT license means anyone can use GLM-5 commercially with zero restrictions.
Claude Opus 4.6: Adaptive Reasoning
Anthropic keeps architecture details private. What they've disclosed: Opus 4.6 introduces "adaptive thinking," replacing the earlier extended thinking mode. Instead of manually setting a reasoning budget, the model dynamically decides how much to reason based on task complexity. Effort controls (low/medium/high/max) let you tune the tradeoff between speed and accuracy.
Opus 4.6 also introduces Agent Teams in Claude Code, allowing multiple sub-agents to work in parallel on different parts of a codebase. Context compaction keeps long-running sessions from exceeding memory limits. These features don't exist in the base model API but show up in Anthropic's product layer.
When to Use Which
| Your Situation | Pick | Why |
|---|---|---|
| Budget-constrained team | GLM-5 | 5-45x cheaper depending on usage pattern |
| Complex multi-file refactors | Opus 4.6 | Higher first-pass accuracy, parallel execution, 80.8% SWE-bench |
| Self-hosted / air-gapped | GLM-5 | MIT license, 40B active params, multiple hosting options |
| Processing 200K+ documents | Opus 4.6 | 1M beta context with 76% retrieval accuracy |
| High-volume API usage | GLM-5 | $0.09/hr vs $8/hr adds up fast at scale |
| Safety-critical applications | Opus 4.6 | More extensive safety testing, known refusal patterns |
| Agentic browsing / tool use | GLM-5 | 75.9% BrowseComp, strong MCP-Atlas scores |
| Enterprise with compliance needs | Opus 4.6 | US/EU data residency, SOC 2, established vendor |
| Maximizing accuracy per task | Opus 4.6 | #1 Arena, leads reasoning benchmarks, adaptive thinking |
| Mixed workload routing | Both | Route hard tasks to Opus, easy tasks to GLM-5, save 60-70% |
The Hybrid Approach
Many teams route tasks by difficulty. Complex reasoning, multi-file refactors, and correctness-critical work go to Opus 4.6. Boilerplate generation, documentation, simple bug fixes, and code reviews go to GLM-5. This typically handles 70-80% of workloads on GLM-5, cutting total API spend by 60-70% with minimal quality loss.
Since GLM-5 is MIT-licensed and available through multiple providers, you're not locked into a single vendor. If a cheaper provider appears tomorrow, you switch endpoints. With Opus 4.6, Anthropic sets the price.
Apply Layer Independence
Whichever model you choose, the code edits still need to be applied to files correctly. Morph Fast Apply works as a universal apply layer underneath both models. At 10,500+ tok/sec with 98% first-pass accuracy, it processes diffs from GLM-5 and Opus 4.6 identically. Choose your model based on reasoning quality and cost. Let Morph handle the last mile.
Frequently Asked Questions
Is GLM-5 really 5x cheaper than Claude Opus 4.6?
Yes. GLM-5 costs $1.00 per million input tokens and $3.20 per million output tokens via the Z.ai API. Claude Opus 4.6 costs $5.00/$25.00. On a blended basis (3:1 input/output), GLM-5 runs about 5x cheaper on input and nearly 8x cheaper on output. Third-party providers like DeepInfra offer GLM-5 even cheaper at $1.24/MTok blended.
Which model is better for coding?
Claude Opus 4.6 leads on every coding benchmark with published scores. SWE-bench Verified: 80.8% vs 77.8%. Terminal-Bench 2.0: 65.4% vs 56.2%. In real-world usage, Opus parallelizes file operations and tool calls more efficiently, finishing multi-file tasks roughly twice as fast. GLM-5 is solid for the price, but trails Opus on coding specifically.
Can I self-host GLM-5?
Yes. GLM-5 is fully open-source under the MIT license, available on Hugging Face and ModelScope. The active parameter count is 40B (mixture-of-experts), which makes inference more manageable than the 744B total suggests. Multiple cloud providers (DeepInfra, Novita, SiliconFlow) also offer hosted inference if you don't want to manage infrastructure.
What is GLM-5's context window?
200K input tokens, 128K max output tokens. That matches Opus 4.6's standard tier. Opus pulls ahead with a 1M token beta context window (scoring 76% on multi-needle retrieval at that length). For most coding tasks, 200K is more than sufficient.
Which model ranks higher on Chatbot Arena?
Claude Opus 4.6 Thinking holds #1 overall at 1506 Elo. GLM-5 holds #1 among open-source models at 1451 Elo. The 55-point gap is meaningful in aggregate human preference, but narrows in specific categories like browsing and agentic tasks where GLM-5 excels.
Was GLM-5 really trained without NVIDIA chips?
Yes. Zhipu AI trained GLM-5 entirely on 100,000 Huawei Ascend 910B processors. After the U.S. Entity List designation in January 2025 cut access to NVIDIA hardware, Zhipu accelerated its "sovereign AI" strategy and retrained flagship models on domestic Chinese chips manufactured by SMIC using a 7nm process.
Related Comparisons
Apply Code Edits from Any Model at 10,500+ tok/sec
Morph Fast Apply works underneath GLM-5, Claude Opus 4.6, or any other model. 98% first-pass accuracy on code diffs. Choose your model for reasoning. Let Morph handle the apply step.