DeepSeek V4 vs GLM 5.1: Which Is Better for Coding and AI Agents?

AI Takeaway

• Which model is better overall? DeepSeek V4 Pro is the stronger starting point, combining competitive coding with a 1M-token context window and low API prices.
• Which is better for long-running coding agents? GLM-5.1 is worth testing on ambiguous refactors, repeated terminal feedback, and multi-step engineering.
• Which DeepSeek V4 version matters? Compare GLM-5.1 with V4 Pro for quality. Treat V4 Flash as the cheaper high-volume option.
• What matters beyond benchmark rank? Tests passed, retries, elapsed time, tool-call recovery, and total cost reveal more than a single leaderboard score.
• What is the practical choice? Start with DeepSeek V4 for routine, large-context, and easily verified work. Test GLM-5.1 when sustained execution matters more than the lowest token price.

GLM-5.2 is now available and belongs in a new evaluation; the DeepSeek V4 Pro vs GLM-5.2 comparison covers that newer matchup. GLM-5.1 still matters for existing integrations and migration decisions.

DeepSeek V4 vs GLM-5.1 at a Glance

DeepSeek V4 is a family rather than one model. V4 Pro is the flagship, with 1.6T total parameters, 49B active parameters, 1M context, and 384K maximum output. See the DeepSeek V4 Pro model overview for its reasoning modes and tools.

GLM-5.1 is a 754B open-weight model for long-horizon engineering, with 200K context, 128K output, function calling, structured output, caching, and MCP.

Category	DeepSeek V4 Pro	GLM-5.1
Best fit	High-volume coding, long context, structured automation	Long engineering loops, refactoring, iterative optimization
Context window	1M tokens	200K tokens
Maximum output	384K tokens	128K tokens
Current API price	$0.435 input / $0.87 output per 1M tokens	$1.40 input / $4.40 output per 1M tokens
Tool calling	Yes	Yes
Open weights	Yes	Yes

On paper, DeepSeek has the clearer context and pricing advantage. GLM-5.1 becomes more interesting when a task involves a messy sequence of attempts, tests, and corrections.

Benchmarks: What the Scores Actually Show

Coding and Software Engineering

SWE-Bench Pro is the cleanest model-card comparison. GLM-5.1 reports 58.4, while DeepSeek V4 Pro Max reports 55.4, giving GLM a narrow lead on this repository-level test. DeepSeek also reports 80.6 on SWE-Bench Verified, but the task sets differ.

Across broader coding and reasoning indexes, DeepSeek V4 Pro usually moves back in front. GLM-5.1's better signals tend to appear in repository repair, terminal work, and longer execution paths.

Reasoning, Tools, and Long-Horizon Work

DeepSeek tends to lead on broad reasoning measures such as GPQA and HLE. Its 1M context also helps when files, issue history, tests, and logs accumulate, although irrelevant material still hurts.

GLM-5.1's strongest story is sustained work. Z.AI demonstrates hundreds of iterations, thousands of tool calls, and an eight-hour software-building run. These vendor examples are not neutral proof, but the GLM-5.1 model guide shows clearly what the model was designed to do.

Why Different Tests Name Different Winners

Reasoning effort, model snapshot, prompt, token budget, provider, and agent scaffold can all change the result. A maximum-effort run should not be compared silently with a default mode. Use rankings to create a shortlist, then test a task with a clear definition of done.

Which Model Performs Better on Real Coding Work?

DeepSeek V4 Is Stronger for Frequent, Verifiable Tasks

DeepSeek V4 Pro is easy to justify when output can be checked automatically. Test generation, code review, log diagnosis, extraction, and batch fixes fit this pattern. Its large context reduces chunking, but focused context still beats a careless repository dump.

V4 Flash keeps the 1M context window at much lower rates, making it practical for summaries, classification, routine patches, and background jobs. Choose Pro when deeper planning is likely to prevent costly retries.

GLM-5.1 Can Be Better at Refactoring and Sustained Iteration

GLM-5.1 deserves a fair test when a task starts with uncertainty. Large refactors, dependency-heavy changes, optimization, and repeated terminal feedback reward a model that can revisit its plan.

Hands-on reports split: DeepSeek often feels more precise, while GLM can preserve behavior better through a complicated refactor. Repository and agent setup explain much of the variation. The Kimi K2.6 vs GLM-5.1 comparison shows a similar divide.

Judge the Result You Would Actually Keep

Give both models the same clean commit, prompt, files, tools, permissions, time limit, and acceptance tests. Record:

• Tests passed and regressions introduced
• Number of retries and failed tool calls
• Elapsed time to an acceptable result
• Total input, cached, and output tokens
• Manual edits required before shipping
• Whether the model recovered after a wrong turn

The better model reaches an acceptable result with less cost and supervision, not necessarily the prettier first response.

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Pricing: Cheap Tokens vs Cheap Results

The Rate-Card Difference Is Large

At current first-party rates, DeepSeek V4 Pro costs $0.435 per million uncached input tokens and $0.87 per million output tokens. GLM-5.1 costs $1.40 and $4.40 respectively. These rates can change, so they should be checked again before committing a large workload.

One million fresh input tokens plus 100,000 output tokens cost about $0.52 with V4 Pro and $1.84 with GLM-5.1. The gap compounds as an agent resends tools, files, command output, and errors.

DeepSeek has a clear advantage for scheduled and high-volume work. The DeepSeek V4 Pro vs GPT-5.5 comparison applies the same logic to retry and review costs.

Cost per Completed Task Is the Better Measure

Cheap tokens can still produce an expensive outcome. A failed patch may require another run, another model, and manual repair. A higher rate can be cheaper if it avoids that loop.

A practical calculation is:

Model spend + retries + failed tool calls + human correction time + failure risk

DeepSeek usually wins for routine, reversible work. GLM-5.1 can win a difficult task by avoiding dead ends. Measure both rather than letting the rate card decide.

The Same Model Can Behave Differently Across API Providers

The model name does not guarantee identical service. Routes may expose different snapshots, context limits, quantization, throughput, caching, or reasoning controls.

Record the exact model ID, provider, version, context, reasoning setting, latency, and price. Confirm that tool calling and structured output work the same way, or a route limitation may look like a model weakness.

This matters for DeepSeek V4, where Pro, Flash, reasoning modes, and snapshots are easily confused. Keep the route fixed or disclose the change.

How to Test DeepSeek V4 and GLM-5.1 in MyClaw

A fair comparison needs one environment. MyClaw provides an always-on OpenClaw workspace with files, tools, browser workflows, skills, and schedules, keeping the test focused on completed work.

Step 1: Pick a Task That Can Actually Fail

Choose work you genuinely need: fix a failing test, review a pull request, investigate a log, or refactor a module. Define the required tests, time limit, allowed tools, and protected files before starting.

Get Started

Step 2: Lock the Playing Field

Run both models from the same commit with identical context, prompt, permissions, tools, and budget. Use clean sessions so the second run does not inherit clues from the first.

Match reasoning settings as closely as possible and label maximum-effort runs. The comparison only needs to be fair enough to guide your workflow.

Step 3: Keep the Result You Would Ship

Compare tests, time, retries, token cost, tool failures, and manual changes. Keep the model with the lower cost per accepted result, and use the other as a fallback where it performs better.

Which Model Should You Choose?

Choose DeepSeek V4 If

• You run frequent coding or automation tasks.
• A 1M context window reduces chunking.
• Tests or schemas can verify the output.
• API cost controls whether the workflow scales.

Use V4 Flash for high-volume, lower-risk work. Move to V4 Pro when deeper reasoning is likely to reduce retries.

Choose GLM-5.1 If

• The task involves ambiguous refactoring or dependent steps.
• The agent must absorb repeated terminal feedback.
• An existing Z.AI integration works reliably.
• Your tests show fewer retries despite the higher rate.

Use Both If the Risk Changes by Task

A permanent winner is unnecessary. DeepSeek can handle routine work while GLM takes selected long-horizon tasks. Route at task boundaries, collect completed-task data, and add complexity only when a repeatable advantage appears.

Conclusion: Choose by Finished Work, Not the Leaderboard

In the DeepSeek V4 vs GLM 5.1 decision, DeepSeek is the stronger starting point for most coding and agent work. Its 1M context, low price, and broad capability make Pro and Flash easier to scale. GLM-5.1 remains valuable for long iterative engineering.

The most reliable decision is also the simplest: run the same task in the same environment, count retries and manual repair, and choose the model that produces work you can actually keep.