DeepSeek V4 Pro vs GLM-5.2: Which Is Better for Coding and AI Agents?

AI Takeaway

• Which model is stronger overall? GLM-5.2 leads the matched max-effort comparison and is the better first test for long-horizon engineering. DeepSeek V4 Pro delivers better value when cost and volume matter more.
• Which model is cheaper? DeepSeek V4 Pro costs $0.435 per million uncached input tokens and $0.87 per million output tokens. GLM-5.2 costs $1.40 and $4.40 respectively.
• Which is better for coding agents? GLM-5.2 suits large refactors and long tool loops. DeepSeek is easier to justify for frequent, clearly scoped work.
• Do they have the same context window? Both support one million tokens. DeepSeek allows up to 384K output, while GLM-5.2 allows 128K.
• What is the most practical setup? Use DeepSeek for routine execution, move difficult work to GLM, and adjust that split using completed tasks rather than benchmark rank alone.

DeepSeek V4 Pro vs GLM-5.2 at a Glance

Both models offer open weights, an MIT license, reasoning controls, tool use, and a one-million-token context window. DeepSeek V4 Pro is the low-cost workhorse. GLM-5.2 costs more but carries a stronger long-horizon capability signal.

Category	DeepSeek V4 Pro	GLM-5.2
Best fit	High-volume coding, structured automation, cost-sensitive agents	Long-horizon engineering, large refactors, difficult multi-step work
API price per 1M tokens	$0.435 uncached input / $0.003625 cached input / $0.87 output	$1.40 input / $0.26 cached input / $4.40 output
Context / maximum output	1M / 384K	1M / 128K
Reasoning	Non-thinking, thinking, and maximum-effort settings	Non-reasoning, High, and Max effort
API model ID	deepseek-v4-pro	glm-5.2

The short version is simple: GLM gives a difficult task a better chance of surviving a long tool loop; DeepSeek lets you run far more tasks for the same budget.

DeepSeek V4 Pro vs GLM-5.2 Pricing: What You Actually Pay

Current API Prices

DeepSeek is about 3.2 times cheaper on uncached input and 5.1 times cheaper on output at current first-party rates. Its cache-hit price is the more dramatic difference: $0.003625 per million cached tokens, compared with $0.26 for GLM-5.2. Agent loops often resend stable instructions, tool schemas, project context, and conversation history, so caching can materially change a monthly bill.

GLM also has a Coding Plan, but it should not be mixed into the pay-as-you-go table. The plan uses quotas and a coding-only endpoint; the regular API charges by tokens. Third-party providers add their own prices, context limits, and performance.

For a quick example, a coding-agent run with 40,000 uncached input tokens and 8,000 output tokens costs about $0.024 with DeepSeek and $0.091 with GLM before retries or provider markups. That is a clear DeepSeek win on the rate card.

Cost per Completed Task

The rate card is not the final bill. A cheap run stops being cheap when it needs several retries, produces malformed tool arguments, or leaves an hour of cleanup.

Cost per accepted task = (model spend + review time × hourly cost + repair cost) ÷ accepted results

Track the result you would actually keep: tests passed, elapsed time, total tokens, tool failures, and manual edits. The same principle appears in the recent comparison of DeepSeek V4 Pro and Sonnet 4.6: lower token prices matter most when the outcome is easy to verify.

Coding and Agent Benchmarks: GLM Leads the Long Game

No benchmark reproduces a full coding workflow, but matched results still show a useful pattern.

Benchmark	GLM-5.2	DeepSeek V4 Pro	Source Type
Intelligence Index	51	44	Independent, matched max effort
GDPval-AA v2	1,524	1,328	Independent
SWE-bench Pro	62.1%	55.4%	Vendor-reported shared table
HLE with tools	54.7%	48.2%	Vendor-reported shared table
Tool-Decathlon	48.2	52.8	Vendor-reported shared table
HMMT February 2026	92.5%	95.2%	Vendor-reported shared table

Where GLM-5.2 Pulls Ahead

GLM leads the independent indexes, SWE-bench Pro, and reasoning-with-tools result. It is the stronger first test when an agent must inspect an unfamiliar repository, change dependent files, run tests, recover from failures, and preserve the original goal.

Where DeepSeek Keeps an Edge

DeepSeek leads Tool-Decathlon and HMMT in the shared table. It also publishes strong competitive-programming results, although GLM has no comparable score there. Its lower price is the clearer advantage for bounded implementation and frequent tasks with objective tests.

Test setup matters. High and Max reasoning use different amounts of time and tokens. Terminal-Bench versions are not interchangeable, and the coding harness controls context, retries, and recovery. Match the effort, provider, benchmark version, and surrounding system before treating a small difference as meaningful.

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1M Context, Output Limits, and Tool Use

The Same Context Number Does Not Mean the Same Experience

A one-million-token window is capacity, not guaranteed attention. Loading an entire repository may bury the crucial file. Retrieve relevant files, keep current instructions close, and compact older history before it becomes noise.

DeepSeek's 384K maximum output is useful when a job genuinely needs a large generated artifact, extensive structured data, or a broad code transformation. GLM's advantage is less about emitting more text and more about maintaining a coherent plan across dependent steps. Most coding jobs will hit neither output limit, so accuracy and recovery matter more than the raw ceiling.

Test the Endpoint, Not Just the Model Name

The same model can feel different across providers. Latency, speed, quantization, usable context, disconnects, and tool formatting may change. Measure response time, schema errors, retries, and completion rate on the endpoint you plan to keep.

Both models have open weights, but neither full checkpoint is a casual local install. Before buying enterprise-class GPU hardware, it is usually more sensible to test first-party APIs, compatible providers, or a managed OpenClaw workspace with the tools and files your job requires.

Which Model Should You Choose?

Choose DeepSeek for High-Volume, Verifiable Work

DeepSeek is the easier default for test generation, code explanation, extraction, documentation, batch review, repository summaries, and scheduled checks. These jobs are frequent, reversible, and easy to score. Low input, output, and cache prices compound quickly when an agent runs all day.

Choose GLM for Long-Horizon Engineering

GLM makes more sense for repository takeover, large refactors, difficult debugging, architecture changes, and tasks that require planning across many files. Its higher rate is easier to accept when one lost constraint can invalidate an hour of tool use.

Route Both When the Work Changes

A permanent winner is unnecessary. Start with DeepSeek for routine execution, move ambiguous or multi-stage work to GLM, and keep the second model available for fallback. A practical coding-agent workflow may use DeepSeek for nightly checks and GLM for the migration those checks uncover.

Keep the first routing rule simple. Add conditions only after several real runs show a consistent difference in completion, cost, or cleanup.

How to Test Both Models on Real Agent Work

Two browser tabs do not create a fair agent comparison. The runtime, files, tools, permissions, budget, and finish line need to remain fixed. MyClaw gives you an always-on agent environment where you can test models through supported or compatible providers without rebuilding the rest of the setup.

Step 1: Give Both Models a Job That Can Fail

Choose one backlog task: fix a failing test, review a pull request, complete a migration, or produce a cited report. Define three pass conditions plus a time and spending limit. “It looks good” is not a pass condition.

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Step 2: Keep Everything Fixed Except the Model

Run two fresh sessions with the same prompt, files, tools, permissions, reasoning level, and definition of done. Let both models inspect the environment, call tools, encounter errors, and revise their work. Record completion, tool errors, retries, elapsed time, token use, and manual cleanup.

If the brief improves after the first attempt, rerun both models with the improved version. Otherwise, the second model receives an unfair advantage.

Step 3: Promote the Winner and Keep a Fallback

Use the better model as the default for that workflow. Save the instructions as a reusable skill or scheduled task, and keep the other model for escalation or outages. Test a second task before making a broader rule.

Conclusion: DeepSeek for Efficiency, GLM for the Long Game

The DeepSeek V4 Pro vs GLM-5.2 decision comes down to the cost of being wrong. DeepSeek is the stronger value for frequent, well-defined work with objective checks. GLM is the stronger first test for difficult engineering that must stay coherent across many steps.

Keep the environment fixed, compare accepted results, and include retries and cleanup in the total. The model that finishes useful work with the least supervision deserves the job, regardless of which name sits higher on a leaderboard.