GLM 5.2 vs DeepSeek V4: The Open-Weights Coding Showdown (2026)

For the first time in a decade of LLM history, the most interesting open-weights showdown in coding doesn't involve a US lab. Z.ai's GLM 5.2 (June 13, 2026) and DeepSeek's V4 are both genuinely usable, both come with permissive open weights, and both happen to be Chinese. They take different paths to the same destination: GLM 5.2 bets on context window and agent loop quality; DeepSeek V4 bets on raw per-token cost and battle-tested reliability. Here's how they compare where it matters.

GLM 5.2 vs DeepSeek V4: at a glance

How do the coding benchmarks actually compare?

Honest answer: at the time of writing (mid-June 2026), GLM 5.2 has no vendor-published benchmarks. DeepSeek V4 does. Comparing on equal footing means looking at the parent model (GLM 5.1) for one side and V4 for the other.

What we know about GLM 5.1 (the most recent peer-reviewed version of the family):

• SWE-Bench Pro: 58.4 (state-of-the-art at the time, narrowly ahead of GPT-5.4 and Claude Opus 4.6)
• Terminal-Bench 2.0: 63.5 standalone, 66.5 with Claude Code scaffolding
• CyberGym: 68.7
• τ³-Bench: 70.6
• MCP-Atlas: 71.8

What we know about DeepSeek V4:

• SWE-bench Verified: ~88% on the favourable scaffold, ~76% on the standard one
• LiveCodeBench: ~85% Pass@1
• HumanEval: 95%+
• Strong on multi-language tasks and a notable lead on Python repo refactors

The pattern: DeepSeek V4 looks stronger on the classic single-shot code-generation benches; GLM 5.1 (and presumably 5.2) leads on the long-horizon, multi-step agentic benches. If your workload is “produce a 200-line patch given a complete spec,” DeepSeek V4 has more public mileage. If your workload is “agent loops over a repo for 4 hours and ships a feature,” GLM is the better-aimed model.

Is the 1M context window actually useful?

It depends on whether you're doing repo-scale or task-scale work.

For a typical SaaS bug fix — read three files, change two, write a test — 128K tokens is plenty. DeepSeek V4 handles this all day and the price-per-task is materially lower than GLM. The 1M window is wasted.

For an agent that wants to understand a 200-file service before proposing a refactor, the math flips. At ~3K tokens per file, 200 files is 600K tokens — over five times DeepSeek V4's standard window. You either use RAG (which costs you context fidelity), prune aggressively (which costs you correctness), or wait for the model that fits the input. GLM 5.2 is now that model.

That's the actual question to ask yourself before choosing: does your agent need to think across an entire codebase, or against a focused slice of it? The answer determines which model is right for the job before pricing even enters the picture.

What do the token economics look like?

DeepSeek V4 has the most aggressive open-weights pricing in the market. V4-Flash at $0.14 input / $0.28 output is the cheapest serious coding model you can call from an API today — about 36× cheaper than GPT-5.5 on input and over 100× cheaper on output. V4-Pro at $1.74 / $3.48 is still less than half of Claude Sonnet on output.

GLM 5.2's standalone API arrives the week after launch; Zhipu hasn't disclosed pricing yet. Based on GLM 5.1's API rates (and consistent with the team's positioning as a frontier-but-affordable alternative), expect somewhere in the $1-2 input / $3-6 output range. That's likely to land between V4-Flash and V4-Pro on per-token cost.

The flat-rate side is where GLM is structurally different. The GLM Coding Plan (Lite / Pro / Max / Team) gives you predictable monthly bills for predictable agent volume. DeepSeek doesn't offer a similar plan — you pay per token regardless. For shops with bursty inference (one engineer kicks off a refactor at 3pm and burns 50M tokens), GLM's flat plan caps the bill cleanly. For shops with steady, predictable inference, DeepSeek V4's per-token rate is hard to beat.

Self-hosting: the two paths compared

Both ship open weights, both are self-hostable, both are roughly the same size class (700B-class MoE for GLM 5.2; DeepSeek V4 is also a large MoE).

DeepSeek V4 has been in the wild for months. The vLLM, TensorRT-LLM, and SGLang teams have shipped multiple rounds of optimizations specifically for V4's MoE structure. Hosted inference is available from every major provider (Together, Fireworks, DeepInfra, Groq, OpenRouter), often at sub-$0.50 per million tokens for V4-Flash equivalents. Quantized variants (4-bit, FP8) are well-tested and ship with usable quality.

GLM 5.2's weights drop the week after launch (target: late June 2026). Inference-engine support typically follows by 1-2 weeks for major engines. Hosted endpoints arrive on similar timelines. Plan for an extra month of maturation before treating GLM 5.2 self-hosting as production-grade. If you need open weights today, DeepSeek V4 is the safer call. If your timeline is “Q3 2026 or later,” GLM 5.2 is fully in scope.

For the operational playbook around self-hosting decisions at this scale, see our self-hosting LLMs guide.

Does multi-modal matter here?

If your coding work touches screenshots, design specs, mockups, or any image-to-code workflow: DeepSeek V4-Pro is multi-modal. GLM 5.2 is text + code only. That's a hard wall.

For pure-text agentic coding — the case for most engineering teams — it's a non-issue.

Who should pick GLM 5.2?

• Repo-scale agents. 1M context is the only realistic answer for agents that need full-codebase awareness without RAG.
• Shops already on the GLM Coding Plan. The flat-rate predictability + the bundle of Z.ai tooling around it is the package.
• Teams that want the newest open-weights research target. MIT license, fresh release, room to fine-tune on internal code.

Who should pick DeepSeek V4?

• High-volume API workloads. V4-Flash's $0.14 / $0.28 pricing is the lowest you'll find for a serious coding model. Burst a million bug-triage runs through it at near-zero cost.
• Multi-modal coding tasks. Image-to-code, screenshot-to-implementation, design-spec-to-component — V4-Pro is the only option of the two.
• Production agents that need the model today. The hosted inference and self-hosting ecosystem is mature. GLM 5.2's hosted endpoints will take 1-2 weeks to catch up.
• Teams that need single-shot code generation more than long-horizon agent loops. The published SWE-bench Verified numbers are public and strong.

The decision in a line

If you've already mapped your workload as either “repo-scale autonomous agent” or “high-volume single-shot completion,” the answer is direct: GLM 5.2 for the former, DeepSeek V4 for the latter. If your workload is somewhere in between, run both side-by-side on a representative task this week. The per-task cost gap and the per-task quality gap both reveal themselves in fewer than 10 runs.

FAQ

Is GLM 5.2 better than DeepSeek V4 for coding?

On long-horizon agentic coding (multi-step refactors, repo-scale planning), GLM 5.2 inherits the agentic-RL training that made GLM 5.1 a leader on SWE-Bench Pro and Terminal-Bench 2.0, plus a 1M context window. On single-shot code generation and high-volume API calls, DeepSeek V4 has more public mileage, a proven SWE-bench Verified score in the high 80s, and a per-token rate that's hard to beat.

Which is cheaper, GLM 5.2 or DeepSeek V4?

DeepSeek V4-Flash at $0.14 input / $0.28 output per M tokens is currently the cheapest serious coding API. GLM 5.2's standalone API pricing isn't disclosed yet but is expected in the $1-2 / $3-6 range based on GLM 5.1's API. For predictable bulk inference, the GLM Coding Plan's flat-rate tier is the cleaner economics; for pay-as-you-go, DeepSeek V4 wins.

Can I self-host both models?

Yes. DeepSeek V4 has months of inference-engine optimization behind it. GLM 5.2's MIT-licensed open weights drop the week after launch; engine support is typically 1-2 weeks behind. Both need 4-8 H100s for serviceable serving at full context.

Does DeepSeek V4 support 1M-token context?

No. DeepSeek V4 standard context is 128K, extended to 256K on V4-Pro. For full-repo awareness on large codebases, GLM 5.2's 1M window is the only realistic option between these two.

Does GLM 5.2 support image inputs?

No. GLM 5.2 is text + code only. For image-to-code workflows, DeepSeek V4-Pro is the choice.

Related reading

• GLM 5.2 Just Launched: 1M Context, Coding-First (Day-One Brief)
• DeepSeek V4 Complete Guide 2026
• Open-Source LLMs Landscape 2026
• AI Coding Agents Complete Guide 2026