When I first spun up a GLM-4.7 vs DeepSeek workflow for coding, I expected the usual: slightly different logos, roughly the same experience. Instead, I ended up with two very different personalities on my screen.

GLM-4.7 felt like the senior engineer who over-explains but almost never breaks production. DeepSeek behaved more like the speed-obsessed intern who ships fast and cheap, and occasionally forgets an edge case. Both are Chinese open-weight models, both marketed as coding-capable, and both are now creeping into Western dev and indie creator workflows.

I spent a week throwing real tasks at them, bug fixes, multilingual code comments, API wrappers, and long-context refactors, to see how GLM-4.7 vs DeepSeek actually compares in practice, not just on paper.

The Open-Weight Coding Model Showdown

Two Chinese open-weight models

Let's set the stage.

In this GLM-4.7 vs DeepSeek comparison, I tested:

• GLM-4.7 (358B dense, open-weight, via API + local quantized run)
• DeepSeek V3.2 (Mixture-of-Experts, sparse, also open-weight via community backends)

Both position themselves as:

• Strong at coding and reasoning
• Competitive or better than many proprietary models on benchmarks
• Friendly to self-hosting and regional deployment (especially in Asia)

For my tests, I focused on coding workflows indie builders actually use:

• Fixing real bugs from a small Flask + React app
• Generating TypeScript types from messy JSON
• Writing quickly-deployable scripts (Python, JS)
• Refactoring with long context (40–80K tokens of mixed code + docs)

Why this matters for global developers

The interesting thing about these two isn't just performance, it's who they're optimized for.

• GLM-4.7 feels tuned for robustness and long-form reasoning. Think: big refactors, long technical docs, structured code explanations.
• DeepSeek V3.2 feels tuned for throughput and cost. Perfect for AI coding agents, batch code generation, or high-volume API usage.

If you're a solo dev, indie SaaS founder, or content person dabbling in tools, the GLM-4.7 vs DeepSeek decision becomes a trade-off between stability vs cost-speed combo, and that shows up quickly when you look at benchmarks and actual runs.

Benchmark Comparison

SWE-bench Verified

I don't have a full SWE-bench lab in my living room (yet), but I did a small replication-style test on 20 GitHub issues:

• 10 backend (Python, Flask, Django-style)
• 10 frontend (React + TS)

Success = patch applied, tests pass, behavior matches description.

In my mini SWE-like run:

• GLM-4.7 solved 13/20 issues (65%)
• DeepSeek solved 10/20 issues (50%)

Not a scientific SWE-bench-verified score, but directionally:

• GLM-4.7 is better at reading long issue threads and inferring the real root cause.
• DeepSeek is more likely to give plausible but slightly off fixes, especially on multi-file changes.

If your coding workflow leans heavily on "read this long GitHub issue, understand the context, and patch safely," GLM-4.7 clearly pulled ahead in my tests.

Multilingual coding performance

I also tested multilingual prompts:

• Problem explained in Chinese, code in Python
• Problem described in English, existing comments in Japanese
• Variable naming hints in Spanish

Rough result pattern:

• GLM-4.7 produced cleaner, more consistent naming when the description and variable hints were in different languages.
• DeepSeek sometimes "locked into" the language of the initial prompt and partially ignored later instructions in another language.

For multilingual coding tasks, I'd rate it like this:

• GLM-4.7: ~9/10 for following mixed-language instructions
• DeepSeek: ~7/10, still good, but a bit more brittle when contexts switch languages mid-prompt.

Math and reasoning capabilities

For math-heavy coding tasks (dynamic pricing logic, algorithm complexity explanations, small DP problems), I threw 30 problems at both models:

• 10 pure math
• 10 math-in-code (Python)
• 10 reasoning + code (e.g., "explain, then carry out Dijkstra")

Result snapshot:

• GLM-4.7: ~83% fully correct (25/30)
• DeepSeek: ~70% fully correct (21/30)

The difference wasn't just raw correctness:

• GLM-4.7 gave clearer intermediate reasoning, and the code matched its reasoning most of the time.
• DeepSeek occasionally had correct reasoning but slightly wrong code, especially around off-by-one and boundary conditions.

If you're doing algorithm-heavy work or data tasks where math errors hurt, GLM-4.7 felt safer.

Architecture Deep Dive

GLM-4.7: 358B dense model

GLM-4.7 is a fully dense ~358B parameter model. In simple terms: every token passes through the whole network. No experts, no routing.

What this typically means in practice:

• More predictable behavior across task types
• Heavier compute footprint per token
• Often smoother long-context reasoning because all layers see everything

In my runs, GLM-4.7 felt "heavy but thoughtful." Slightly slower, but noticeably more stable when the prompt was messy or over-explained (which, let's be honest, is how real prompts look).

DeepSeek V3.2: MoE with sparse attention

DeepSeek V3.2 uses a Mixture-of-Experts (MoE) design with sparse activation:

• Only a subset of "experts" activate per token
• Lower compute cost per token
• Potentially more capacity overall for the same hardware budget

In practice, this gives DeepSeek its speed and cost advantage but also introduces some quirks:

• Occasionally "snaps" to a certain style or pattern
• Rare, but I saw inconsistent behavior on nearly identical prompts

You definitely feel the MoE character: it's fast, and sometimes brilliantly so, but a bit more "personality-driven" than a big dense model.

Implications for inference and deployment

The GLM-4.7 vs DeepSeek architectural difference matters if you:

• Run your own GPU stack
• Care about latency under load
• Need predictable behavior across a team

Rules of thumb from my tests:

• For API-only use, DeepSeek usually wins on cost/speed, GLM-4.7 wins on stability.
• For self-hosting, DeepSeek is viable on fewer high-end cards (MoE), while GLM-4.7's dense nature wants more raw GPU and memory.

If you're an indie builder deploying to a single A100 or a cluster of consumer GPUs, DeepSeek will generally be easier to scale cheaply.

Speed and Latency

Time to first token

I measured time to first token (TTFT) over 50 requests each, via similar-quality hosted endpoints.

Average TTFT on a 2K-token prompt:

• GLM-4.7: ~1.3–1.5 seconds
• DeepSeek: ~0.7–0.9 seconds

So DeepSeek starts talking roughly 40–50% faster. When you're in a tight feedback loop ("fix this function… no, not like that"), it feels noticeably snappier.

Tokens per second

For throughput, I tested 1K–2K completion lengths.

Average tokens/sec:

• GLM-4.7: 25–30 tokens/sec
• DeepSeek: 45–55 tokens/sec

That's about 60–80% faster generation with DeepSeek in my environment.

If you're building an AI coding assistant that streams suggestions, DeepSeek's speed is real, not marketing.

Long-context performance

But speed isn't the whole story.

On 40K+ token contexts (large repos, long design docs), I saw this:

• GLM-4.7 stayed coherent longer, with fewer "context hallucinations."
• DeepSeek stayed fast but sometimes mis-read older parts of the context or over-weighted the last few screens of code.

For a large 80K-token refactor prompt:

• GLM-4.7: 3 minor issues, but followed file-level constraints correctly
• DeepSeek: 6 issues, including editing a file I explicitly said to leave untouched

So in a long-context GLM-4.7 vs DeepSeek scenario, GLM-4.7 is slower but more trustworthy when you're juggling huge codebases.

Cost Analysis

API pricing comparison

Exact numbers will vary by provider, but the pattern I saw consistently:

• DeepSeek-style MoE endpoints were usually 30–60% cheaper per 1M tokens than GLM-4.7-class dense endpoints.
• In one hosted setup, generation for DeepSeek was about $0.60 / 1M output tokens, while GLM-4.7 sat closer to $1.10 / 1M.

If you're running:

• A side project with low volume → both are affordable
• A SaaS with millions of tokens/day → DeepSeek's advantage compounds very fast

Self-hosting GPU requirements

Rough deployment picture from my own experiments and docs:

• GLM-4.7
  • Full precision: multiple high-memory GPUs (not indie-friendly)
  • 4-bit/8-bit quantized: still heavy: think 2–4 × 80GB GPUs for smooth high-concurrency
• DeepSeek V3.2
  • MoE helps: fewer active parameters per token
  • Reasonable deployments on 2 × 40–80GB cards for mid-scale usage

If you just want a hobby deployment on a single 3090/4090 at home, both will likely need heavy quantization and compromises, but DeepSeek is the more realistic choice.

Effective cost per 1M tokens

Taking hardware + electricity + latency into account, my rough effective cost ratio was:

• DeepSeek: baseline cost = 1.0x
• GLM-4.7: about 1.4–1.8x effective cost per 1M tokens

So from a pure GLM-4.7 vs DeepSeek cost perspective:

• DeepSeek wins for high-volume API workloads, agents, bulk doc generation.
• GLM-4.7 makes more sense when each call "matters" more than the raw token price, e.g., critical refactors, customer-facing code, complex reasoning jobs.

This cost–quality trade-off is exactly what we deal with in production at Macaron. When you’re running millions of inferences, picking a single “best” model rarely makes sense.

We route different tasks to different models based on speed, cost, and failure tolerance — so users never have to think about MoE vs dense, or cents per million tokens. They just get fast, reliable mini-apps.

If you’re curious what this kind of model routing looks like in a real product, Macaron is one concrete example.

Code Quality in Practice

Python, JavaScript, and TypeScript output

For day-to-day indie dev work, this is the part that actually matters.

Across ~50 coding tasks:

• Python: GLM-4.7 tended to produce slightly more idiomatic code (better use of context managers, logging, typing). DeepSeek was fine, but more "tutorial-style."
• JavaScript: Very close. DeepSeek occasionally used slightly older patterns (var-esque thinking). GLM-4.7 leaned modern but verbose.
• TypeScript: GLM-4.7 was clearly better at type inference and generics. DeepSeek would sometimes ignore edge-case nullability or optional fields.

If your stack is TS-heavy, I'd lean GLM-4.7.

Error handling patterns

This is where GLM-4.7 quietly impressed me.

• GLM-4.7:
  • Used structured error handling more often (custom error classes, typed guards)
  • Added reasonable log messages without going full log-spam
• DeepSeek:
  • Faster to ship a working happy-path solution
  • Sometimes under-specified error branches or generic catch (e) patterns

In production-ish workflows, this matters. Debugging a generic Exception without context is pain: GLM-4.7 spared me some of that.

Documentation generation

For docstrings, README snippets, and inline comments:

• GLM-4.7 wrote more human-readable explanations with better structure (sections, bullet lists, examples).
• DeepSeek produced shorter, more compact descriptions, which is nice for quick internal docs but less so for tutorials or user-facing guides.

On a doc generation benchmark I improvised (10 functions, ask both models for full docstrings + usage notes):

• GLM-4.7: I kept ~80% of the content with light editing
• DeepSeek: I kept ~60%: more rewrites needed for clarity and tone

If you create content or developer docs around your code, GLM-4.7's output just felt closer to "publishable with edits" vs "draft I have to heavily rewrite."

When to Choose GLM-4.7

Need for very long outputs (128K)

If your workflow lives in long context, 128K tokens of code, notes, specs, and logs, GLM-4.7 is the safer pick.

In mixed-context tests:

• GLM-4.7 respected file boundaries, constraints, and style rules across 60–90K-token prompts.
• DeepSeek stayed fast but made more context mistakes as prompts grew.

For:

• Full-project refactors
• Large design doc reviews
• Big-batch documentation generation from code

GLM-4.7 just behaved more like a careful senior dev reading everything before touching the keyboard.

Stronger frontend and UI sensibility

This was a surprise: on frontend/UI tasks, GLM-4.7 often felt more "tasteful."

Examples:

• React components with reasonable prop naming
• Better inline comments explaining why a piece of UI logic existed
• More consistent CSS/utility class patterns when given a brief style guide

DeepSeek could absolutely build the same components, but GLM-4.7 more often produced code I'd be comfortable dropping straight into a production-ish frontend repo.

So if your main use case is:

• UI-heavy apps
• Design-system-aware components
• Documentation + examples for your frontend

GLM-4.7 is likely the better default in the GLM-4.7 vs DeepSeek decision tree.

When to Choose DeepSeek

Extreme cost optimization

If your main KPI is "tokens per dollar", DeepSeek is built for you.

Typical cases where I'd pick DeepSeek first:

• AI coding agents that run hundreds of small calls per user session
• Bulk code generation (SDKs for many languages, boilerplate, migration scripts)
• Internal tools where occasional minor mistakes are acceptable

In my side-by-side logs over ~5M tokens:

• DeepSeek cost ~45% less than GLM-4.7 for similar workloads.
• Error rate was higher but still acceptable for non-critical paths.

Fastest possible inference speed

If your app lives or dies on latency, think real-time suggestion panels or chatty assistant UIs, DeepSeek's speed is hard to ignore.

In a realistic "autocomplete while I type" setup:

• DeepSeek felt nearly "instant" once warmed up.
• GLM-4.7 was usable but noticeably slower, especially on first requests.

So my personal rule of thumb for GLM-4.7 vs DeepSeek:

• Pick GLM-4.7 when: correctness, long context, and code quality matter more than cost.
• Pick DeepSeek when: you're scaling hard, want maximum throughput, and can accept a bit more babysitting.

If you're still unsure, start with DeepSeek for exploration and bulk generation, then switch critical paths (prod refactors, customer-facing logic) to GLM-4.7 once the shape of your system is stable.

And, as always with these models: log everything, diff everything, and never skip tests just because the AI sounded confident.