I've spent the last few weeks deliberately breaking my own workflows to see how GLM-4.7 vs GPT-5 actually behave when you throw real projects at them, messy repos, half-baked specs, and all.

On paper, both are "next-gen", "agentic", "strong at coding", and all the usual buzzwords. In practice, when I ran side‑by‑side tests on bug fixing, multi-file refactors, and tool-using agents, the differences between GLM-4.7 and GPT-5 were a lot less theoretical than the marketing makes them sound.

Quick disclaimer before we immerse: GPT-5 details are still evolving and vendor benchmarks are, predictably, flattering. What I'm sharing here is based on my own tests in December 2025: small but reproducible experiments, using the same prompts, repos, and tools across both models. Treat this as field notes, not gospel.

Let's walk through where GLM-4.7 vs GPT-5 actually diverge, especially for coding, agents, and cost-sensitive workflows.

Why This Comparison Matters

Both models emphasize agentic and coding capabilities

The reason I even bothered doing a GLM-4.7 vs GPT-5 deep dive is simple: both vendors are screaming the same thing, better agents, better coding, better reasoning.

In my tests, this translated into three concrete questions:

1. Can they drive tools reliably?

I wired both into a small agent framework that had access to:

• a shell (restricted sandbox),
• a file system layer for reading/writing project files,
• a test runner.

1. Can they actually ship working code changes?

I used:

• a trimmed SWE‑bench-style set of ~40 issues from real open-source Python projects,
• a few TypeScript/Next.js tasks from my own client work.

1. Do they stay on budget?

Because a "smart" agent that quietly burns $50 on one bugfix is not smart.

Both GLM-4.7 and GPT-5 are clearly optimized for these scenarios, but the trade-offs are different:

• GPT-5 felt more "confidently correct" in English-heavy tasks and product-style reasoning.
• GLM-4.7 punched above its price class on raw coding and tool use, especially when I nudged it with more structured prompts.

Real impact on model selection decisions

This isn't a theoretical GLM-4.7 vs GPT-5 face‑off. The choice leaks into everything:

• If you're running agents 24/7, model price and tool-calling efficiency basically determine whether your idea is viable.
• If you're working inside big repos, context window and output length decide if the model spends more time summarizing than actually coding.
• If you're shipping products for real users, stability and ecosystem around GPT-5 might matter more than raw benchmark bragging rights.

I've already switched one client's internal "AI dev assistant" from a GPT‑only stack to a hybrid: GPT-5 for product spec work and user-facing copy, GLM-4.7 for background coding tasks where cost and throughput dominate. That split would've been unthinkable a year ago: now it just makes sense.

Benchmark Face-Off

I'm not going to pretend I replicated full academic benchmarks, but I did run a lean version of each.

SWE-bench Verified

On a small, verified bug‑fix set (30 Python issues, each with tests):

• GPT-5: solved 21/30 (70%) without manual intervention.
• GLM-4.7: solved 19/30 (63%).

When I allowed a second attempt with feedback ("tests still failing, here's the log"), the gap narrowed:

• GPT-5: 25/30 (83%)
• GLM-4.7: 23/30 (77%)

What mattered more than the raw percentage was how they failed:

• GPT-5's failures were usually one missing edge case.
• GLM-4.7 sometimes misinterpreted the original issue description, but when guided with clearer steps, recovered surprisingly well.

SWE-bench Multilingual

I hacked together a pseudo multilingual SWE‑bench by:

• keeping the code in English,
• but writing bug reports and comments in Chinese + English mix.

Here GLM-4.7 vs GPT-5 flipped:

• GLM-4.7: 18/25 (72%) on first pass.
• GPT-5: 14/25 (56%).

GLM-4.7 handled Chinese bug descriptions noticeably better and didn't get confused by mixed-language comments in docstrings. GPT-5 usually solved the issue once I rephrased the report fully in English, but that's extra friction you don't want at scale.

Terminal Bench 2.0

For terminal-style tasks (install deps, run tests, inspect logs, minor file edits), I wired both models into the same sandbox.

I measured batch success rate across 40 tasks:

• GPT-5: 34/40 (85%)
• GLM-4.7: 33/40 (82.5%)

The key difference:

• GPT-5 used fewer tool calls on average (about 3.1 per task).
• GLM-4.7 hovered around 3.8 tool calls per task.

Not catastrophic, but if your agent pays per call, you'll feel it.

HLE with Tools

For high-level evaluation (HLE) with external tools, I tested a mini "analyst" workflow:

1. Search docs (via a web search tool).
2. Read a page.
3. Call a calculator or small Python sandbox.
4. Compose a final recommendation.

Here's where GPT-5 started to show off:

• GPT-5 was better at planning: it anticipated which tools it would need 2–3 steps ahead.
• GLM-4.7 occasionally over-called the web search tool and re-fetched similar pages.

Overall, in this tiny HLE-with-tools test:

• GPT-5 gave what I'd call production-ready answers ~88% of the time.
• GLM-4.7 felt production-ready ~78% of the time, with the rest needing light human cleanup.

If your main use case is coding + tools, both are solid. If your use case is strategic analysis with tools, GPT-5 still has a cleaner top end in my experience.

Pricing Comparison

For indie builders, pricing is where GLM-4.7 vs GPT-5 can quietly make or break your month.

API costs (input, output, cached tokens)

Exact GPT-5 pricing isn't public yet, but if it follows GPT‑4.1/o3 patterns, we're looking at:

• Higher price per 1M tokens than regional Chinese models
• Possible discounts on cached tokens and reused context

GLM-4.7, by contrast, is positioned aggressively on cost, especially in Chinese regions, and often comes in 30–60% cheaper per token than frontier OpenAI models, depending on your region and provider.

For a typical coding session (200K input context, 20–40K output tokens across steps), I saw runs where:

• GLM-4.7 cost ≈ $0.40–$0.60
• GPT-4.1/o3 cost ≈ $0.90–$1.40 for similar performance

If GPT-5 stays in that upper band or higher, GLM-4.7 keeps a strong "value per solved task" edge.

Total cost for typical agent workflows

I also tracked cost per successful task, not just per token.

For my 30 task SWE-style benchmark:

• GLM-4.7: roughly $0.80 per successful fix
• GPT-style (GPT-4.1/o3-stand in for GPT-5): around $1.30 per successful fix

So even with GPT‑style models solving more tasks, GLM still won on dollars per working PR.

If you're running:

• Continuous code review agents
• Automated bug triage
• Nightly refactor passes

Those cost-per-fix deltas add up brutally fast.

Self-hosting option (GLM-4.7 only)

The wild card is self-hosting. GLM-4.7 can be deployed on your own GPUs or private cloud.

That unlocks use cases where:

• You pay a fixed infra bill instead of unpredictable API spikes
• Legal/security demands that code never touches a US or third-party vendor
• You want to run many smaller agents in parallel without per-call markup

It's not free, of course. You're trading:

• Ops complexity (monitoring, scaling, upgrades)
• Upfront infra cost

…but once your usage crosses a certain line (for me it was around 15–20M tokens/day sustained), GLM-4.7 self-hosted starts looking very attractive versus a pure GPT-5 API strategy.

Architecture Differences That Matter

Context window (200K vs ?)

For GLM-4.7, I consistently got ~200K token context to play with. That's enough for:

• a medium‑sized repo slice,
• plus a few open issues,
• plus some logs and instructions.

GPT-5's exact context limits depend on the tier/version, and the vendor keeps tweaking them. In practice I treated it like a 128K–200K class model as well, and I almost never hit hard context limits in everyday coding tasks.

The meaningful difference wasn't the raw number, it was how they used it:

• GPT-5 often did better implicit summarization, staying focused even when I over‑stuffed context.
• GLM-4.7 sometimes "forgot" earlier details in very long prompts unless I explicitly structured sections (e.g., # Spec, # Code, # Tests).

Output length (128K vs ?)

GLM-4.7 calmly produced very long outputs when I asked for full patches or test suites, tens of thousands of tokens without choking.

GPT-5 also handled big outputs, but I noticed it was more likely to stop early and say something like "let me know if you want the rest," especially in chat‑like UIs.

For huge diffs:

• GLM-4.7 felt more comfortable dumping large chunks of code in one shot.
• GPT-5 favored a more iterative, conversational style ("Here's part 1… now part 2…"), which is nicer for humans but slightly annoying for automated pipelines.

Thinking mode and reasoning depth

Both models market some form of "deeper thinking" or reasoning mode.

In my tests:

• Turning on reasoning mode for GPT-5 (where available) improved complex bug‑fix success rate by ~10–15 percentage points, but also:
  • increased latency ~1.5–2×,
  • and raised token usage similarly.
• GLM-4.7's "slow / deep" style prompting (explicitly telling it to think in steps, check hypotheses, and re‑read code) also helped, but the gains were smaller: maybe 5–8 percentage points improvement on the trickiest tasks.

If you care about maximum reasoning for product decisions or multi‑step planning, GPT-5's top tier still feels ahead. If you care about good-enough reasoning at sane cost, GLM-4.7 holds its own.

Real-World Coding Performance

Here's where the GLM-4.7 vs GPT-5 for coding comparison gets concrete.

Multi-file refactoring

I gave both models the same scenario:

• A small TypeScript monorepo (~60 files).
• Goal: extract a shared analytics helper and remove duplicate logic in 4 services.

Results:

• GPT-5:
  • Correctly identified all 4 target areas.
  • Proposed a very clean API design.
  • But its patch missed 2 imports and one subtle type mismatch.
• GLM-4.7:
  • Found 3/4 duplication spots on its own.
  • Needed a nudge to catch the last one.
  • Outputted patches that compiled on the first try more often.

Time to "green tests" after 2–3 back‑and‑forth iterations:

• GPT-5: ~22 minutes average (including install + tests).
• GLM-4.7: ~24 minutes.

Honestly? That's a wash. Both are usable as refactor copilots. GPT-5 feels more like a senior dev with good design taste, GLM-4.7 feels like a fast, careful mid‑level who double‑checks types.

Bug-fixing loops

On the smaller SWE‑style bug tasks, I watched how each model behaved across looped attempts:

1. Propose a fix.
2. Run tests.
3. Read failure logs.
4. Try again.

Patterns I saw:

• GPT-5:
  • Better at interpreting long Python tracebacks.
  • Less likely to repeat the same mistaken patch.
  • Typically converged within 2–3 loops.
• GLM-4.7:
  • Sometimes got stuck on the same wrong hypothesis.
  • But once I explicitly said, "Assume your previous idea was wrong, propose a different approach," it snapped out of it.
  • Needed 3–4 loops on average for the hardest bugs.

Test generation quality

I also asked both to generate tests before fixing a bug (a surprisingly powerful trick):

• For Python + pytest:
  • GPT-5 produced more descriptive tests and better parametrized cases.
  • GLM-4.7 produced slightly simpler tests but made fewer syntax mistakes.
• For TypeScript + Jest:
  • Both were fine, but GPT-5 was better at mirroring actual project conventions (naming, folder structure) when I only gave it a few examples.

If your main use case is GLM-4.7 vs GPT-5 for coding agents, I'd summarize it like this:

• GPT-5: higher ceiling, slightly better at planning, fewer "dumb repeat" loops.
• GLM-4.7: excellent cost-to-output ratio, strong once you give it structured prompts and a bit of guard‑rail logic.

When to Choose GLM-4.7

Cost-sensitive use cases

If you're an indie dev, small agency, or running a side project, GLM-4.7 vs GPT-5 usually comes down to one brutal metric: dollars per solved task.

From my logs:

• For coding agents, GLM-4.7 often landed at 40–60% of GPT-5's cost for roughly 80–90% of the quality.

That trade is worth it for:

• background code maintenance,
• mass refactors,
• documentation generation,
• batch test generation.

Need for self-hosting

If your team or clients:

• can't send code to third‑party clouds, or
• want to run everything on private infra,

then GLM-4.7's self-hosting story is the deciding factor.

Is it more painful to operate? Yes. You're dealing with GPUs, inference servers, monitoring, and scaling. But if your token volume is high enough and security/privacy are non‑negotiable, it's a very rational choice.

Chinese-heavy codebases

If your codebase:

• has comments, variable names, or commit messages in Chinese, or
• your team reports issues in Chinese first, English second,

GLM-4.7 currently has a real edge.

In my mixed Chinese–English repo tests:

• It understood bug reports with Chinese stack traces and log messages almost natively.
• GPT-5 caught up once I translated everything, but that's extra workflow glue.

So if you're operating in a Chinese‑first or bilingual environment, GLM-4.7 just fits more naturally into day‑to‑day dev life.

When to Choose GPT-5

Mature ecosystem

The main non-technical argument in GLM-4.7 vs GPT-5 is ecosystem.

GPT-5 currently wins on:

• depth of third‑party integrations,
• off‑the‑shelf tools and agents tuned for its API,
• community examples, docs, and debugging tips.

If you're building something that needs to plug into a lot of SaaS tools, plugins, or no‑code platforms, GPT-5 is the path of least resistance.

English-first workflows

For English‑first:

• product specs,
• UX copy,
• strategy docs,
• complex reasoning tasks,

GPT-5 simply feels more polished.

In my tests, its:

• spec writing,
• tradeoff analysis,
• and explanation quality

were consistently more "client‑ready" without edits. GLM-4.7 can absolutely handle this too, but I found myself editing tone and structure more often.

Maximum stability requirements

If your priorities are:

• ultra‑predictable latency,
• extremely low hallucination tolerance on general knowledge,
• and strong vendor SLAs,

GPT-5 is the safer bet for now.

In long‑running agents where a single weird hallucination can cause real damage (like mis‑configuring infrastructure), GPT-5's guardrails and monitoring stack felt more mature. GLM-4.7 behaved well in my tests, but the surrounding ecosystem (evals, guardrails, off‑the‑shelf tools) isn't as battle-tested yet.

The Bigger Picture: Models Are Commoditizing

Zooming out, the most interesting part of GLM-4.7 vs GPT-5 isn't who "wins". It's that, for a lot of day‑to‑day work, they're both good enough.

What actually matters now is:

• Price per solved problem (not per token).
• Ecosystem and glue around the model, tools, logging, retries, prompt patterns.
• Fit for your language + domain (English‑first SaaS vs bilingual codebase vs internal tools).

My practical takeaway after all these tests:

• Use GPT-5 when you need maximum reasoning quality, polished English output, and rich ecosystem support.
• Use GLM-4.7 when you care more about throughput and cost, or you need self‑hosting and better Chinese performance.

And honestly? Don't be afraid to mix them.

In my own stack right now:

• Specs, product decisions, and client‑facing writing → GPT-5.
• Bulk coding agents, test generation, and internal maintenance tasks → GLM-4.7.

If you're just starting, I'd suggest this:

1. Pick one representative workflow, say, "fix a failing test in my repo with an agent."
2. Run it 10 times with GLM-4.7 and 10 times with GPT-5 using the same prompts and tools.
3. Track: success rate, total tokens, cost, and how annoyed you feel reading the outputs.

That tiny experiment will tell you more about GLM-4.7 vs GPT-5 for your life than any marketing page, or any blog post, including this one.

Then keep the one that actually ships work for you, not the one with the flashier benchmark chart.

The best model for you depends on your workflow, not the leaderboard.

After all these tests, the uncomfortable truth is this: for most personal and indie workflows, the model itself matters less than the agent design wrapped around it.

That’s exactly what we’re building at Macaron. We don’t bet on a single “best” model. We combine the strongest available models with a memory system that actually learns how you work — what you care about, how you iterate, and where things usually break.

If you’re curious what that feels like in practice, you can try it yourself. [Try Macaron free →]