Chinese AI labs are shipping frontier coding models at a pace that’s hard to keep up with. Within two months of each other, DeepSeek released V4 (April 2026) and Z.ai (Zhipu AI) dropped GLM-5.2 (June 2026) — both open-weight, both with 1M token context windows, and both gunning to be the go-to model for software engineering tasks.
If you’re choosing between them for your coding workflow, here’s what you need to know.
Quick Comparison Table
| Feature | GLM-5.2 | DeepSeek V4-Pro | DeepSeek V4-Flash |
|---|---|---|---|
| Release date | June 13, 2026 | April 24, 2026 | April 24, 2026 |
| Total parameters | 744B (MoE) | 1.6T (MoE) | 284B (MoE) |
| Active parameters | 40B | 49B | 13B |
| Context window | 1M tokens | 1M tokens | 1M tokens |
| Max output | 131K tokens | Not disclosed | Not disclosed |
| Open weights | MIT (coming next week) | MIT (available now) | MIT (available now) |
| Thinking modes | High / Max | Standard | Standard |
| SWE-bench Pro | TBD (GLM-5.1: 58.4%) | 55.4% | — |
| SWE-bench Verified | TBD | 80.6% | — |
| Pricing model | Subscription (~$18/mo) | Per-token API | Per-token API |
Architecture Differences
Both models use Mixture-of-Experts (MoE) architectures, but their designs diverge significantly.
GLM-5.2 is a 744B parameter MoE with 40B active parameters per forward pass. Z.ai has focused on output length — 131K max output tokens is massive and purpose-built for generating entire codebases or long refactoring diffs. The dual thinking modes (High and Max) let you trade latency for reasoning depth, similar to what we’ve seen from other reasoning-mode models.
DeepSeek V4-Pro is considerably larger at 1.6T total parameters with 49B active. DeepSeek’s MoE routing and training efficiency have been their hallmark since V2, and V4 continues that tradition. The model is known for punching well above its weight class on coding benchmarks despite being significantly cheaper than closed competitors.
DeepSeek V4-Flash is the lightweight sibling at 284B total / 13B active — designed for speed and cost efficiency over raw capability.
The key architectural takeaway: GLM-5.2 is more compact but emphasizes long output generation. DeepSeek V4-Pro is larger and has proven benchmark results. Both route only a fraction of their total parameters per token, keeping inference costs manageable.
Benchmark Comparison
This is where things get complicated. DeepSeek V4 has published benchmarks. GLM-5.2 has not — yet.
DeepSeek V4-Pro scored:
- 80.6% on SWE-bench Verified
- 55.4% on SWE-bench Pro
These are strong numbers. For reference, GLM-5.1 scored 58.4% on SWE-bench Pro, which actually exceeds V4-Pro’s published score on the same benchmark. If GLM-5.2 improves on its predecessor (as you’d expect), it could be very competitive.
However, until Z.ai publishes official benchmarks, we’re speculating. The model is two days old. Early community reports suggest it’s strong on multi-file refactoring tasks and particularly good at maintaining coherence over long outputs, but hard numbers are needed before drawing firm conclusions.
Bottom line: DeepSeek V4 has the receipts today. GLM-5.2 has the pedigree (GLM-5.1 was already competitive) but needs to show its cards.
Pricing Breakdown
This is where the comparison gets interesting, because these models use fundamentally different pricing approaches.
DeepSeek V4 — Per-Token API Pricing
| Model | Input | Output |
|---|---|---|
| V4-Pro | $0.435 / M tokens | $0.87 / M tokens |
| V4-Flash | $0.14 / M tokens | $0.28 / M tokens |
DeepSeek’s pricing is absurdly cheap. V4-Pro costs roughly 10x less than comparable closed models, and V4-Flash is practically free for most use cases. If you’re running high-volume batch processing or agentic loops that burn through tokens, DeepSeek’s per-token model is hard to beat.
GLM-5.2 — Subscription (Prompt-Based) Pricing
GLM offers a Coding Plan starting at ~$18/month, which gives you access to GLM-5.2 for coding tasks through their platform. This is a prompt-based subscription — you’re paying for access rather than per-token.
Which Is Cheaper?
It depends entirely on your usage volume:
- Low usage (< 50K tokens/day): GLM’s flat rate may cost more than DeepSeek’s per-token pricing
- Medium usage (50K–500K tokens/day): Roughly equivalent, depending on input/output ratios
- High usage (> 500K tokens/day): GLM’s flat rate becomes significantly cheaper
- Burst usage (agentic loops, batch processing): DeepSeek’s per-token model gives you more control and predictability per task
For individual developers doing daily coding assistance, the $18/month GLM plan is straightforward. For teams running automated pipelines, DeepSeek’s token pricing scales more predictably.
Coding Performance in Practice
Where GLM-5.2 Excels
- Long output generation: 131K max output means it can generate entire modules, full test suites, or comprehensive refactoring diffs in a single pass
- Thinking modes: Max mode is particularly good for complex architectural reasoning and multi-step debugging
- Coherent multi-file changes: Early reports suggest strong performance on changes spanning many files
- Context utilization: The 1M context window paired with long output makes it effective for “read entire codebase, write solution” workflows
Where DeepSeek V4 Excels
- Proven benchmark performance: 80.6% SWE-bench Verified is among the best available
- Cost efficiency at scale: Token-based pricing means you only pay for what you use
- Self-hosting: Weights are available now on Hugging Face, not “next week”
- Flash tier for simple tasks: V4-Flash handles routine completions and simple fixes at near-zero cost
- Ecosystem maturity: Two months of community tooling, fine-tunes, and integrations
Use Case Recommendations
Choose GLM-5.2 if you:
- Need very long outputs (entire file generation, large diffs)
- Want a flat-rate pricing model for predictable costs
- Prefer dual thinking modes for different task complexities
- Are already in the Z.ai ecosystem
- Plan to self-host once MIT weights drop
Choose DeepSeek V4-Pro if you:
- Need proven, benchmarked coding performance today
- Run high-volume agentic workflows where per-token pricing gives you cost control
- Want to self-host immediately (weights already available)
- Need the absolute best SWE-bench scores available
Choose DeepSeek V4-Flash if you:
- Handle mostly routine code completions and simple fixes
- Prioritize speed and cost over raw capability
- Run batch processing jobs where good-enough quality at minimal cost wins
What About Kimi K2.7 and Qwen?
The Chinese coding model landscape is crowded. We’ve covered GLM-5.2 vs Kimi K2.7 and the broader GLM-5.1 vs DeepSeek vs Qwen comparison separately. The short version: Kimi K2.7 is strong on agentic tasks, Qwen remains competitive on general coding, but DeepSeek V4 and GLM-5.2 are currently the top two for pure software engineering workloads.
FAQs
Is GLM-5.2 better than DeepSeek V4 for coding?
Too early to say definitively. GLM-5.2 hasn’t published benchmarks yet. Its predecessor GLM-5.1 scored 58.4% on SWE-bench Pro vs DeepSeek V4-Pro’s 55.4%, so there’s reason to be optimistic — but we need official numbers. For a deeper dive into GLM-5.2’s capabilities, see our complete guide.
Can I self-host both models?
DeepSeek V4 weights (both Pro and Flash) are available now on Hugging Face under MIT license. GLM-5.2 MIT open weights are announced for next week. Both will be fully self-hostable.
Which is cheaper for a solo developer?
DeepSeek V4-Flash at $0.14/M input tokens is likely cheaper for light usage. GLM’s $18/month plan becomes better value if you’re using it heavily throughout the day. Calculate your average daily token usage to decide.
Do they both support 1M context?
Yes. Both GLM-5.2 and DeepSeek V4 (Pro and Flash) support 1M token context windows. GLM-5.2 additionally supports up to 131K output tokens, which is notable for large generation tasks.
Which model is better for agentic coding workflows?
DeepSeek V4-Pro currently has better third-party tool integration due to being available longer. Its per-token pricing also makes cost tracking in agentic loops more transparent. GLM-5.2’s long output capability could be an advantage for agents that need to produce large changes in single steps.
How do these compare to Claude, GPT, or Gemini for coding?
Both models are competitive with top closed models on coding benchmarks while being significantly cheaper and fully open-weight. DeepSeek V4-Pro’s 80.6% SWE-bench Verified puts it in the top tier globally. The main trade-off is ecosystem maturity — closed models have more polished IDE integrations and tooling support, for now.
Final Verdict
If you need a coding model today: DeepSeek V4-Pro. It has proven benchmarks, available weights, mature tooling, and unbeatable per-token pricing.
If you can wait a week: GLM-5.2 is worth evaluating once benchmarks drop and weights become available. The 131K output length, dual thinking modes, and GLM-5.1’s strong SWE-bench Pro score suggest it could be the better model — but “could be” isn’t “is.”
If cost is your primary concern: DeepSeek V4-Flash for per-token or GLM Coding Plan for flat-rate, depending on your usage pattern.
The good news? Both are MIT-licensed. You can try both and decide based on your own workloads. That’s the beauty of open weights — you’re not locked in.
For more on the previous generation comparison, see DeepSeek V4 vs GLM-5.1. For GLM-5.2’s context window capabilities, read GLM-5.2 1M Context Explained.