GLM-5 vs GLM-4.7: Should You Upgrade? (Benchmarks)

Hey, guys. Dora here. I spent a few afternoons in January 2026 swapping a small project between GLM-4.7 and GLM-5 on WaveSpeed. I wasn’t chasing a headline, I wanted to see if the upgrade would quietly make my routine work feel lighter. What follows is what I noticed: architectural shifts, where the new model wins on benchmarks, the latency trade-offs, and a practical checklist if you’re considering migrating. I’ll be specific about tests and behavior, not grand claims.

What changed from GLM-4.7 to GLM-5

Architecture differences (MoE scaling)

The headline architectural change is a wider use of mixture-of-experts (MoE) layers in GLM-5 compared with GLM-4.7. In plain terms: GLM-5 uses more expert sub-networks and routes tokens through a selection of them. That routing makes the model scale up capacity without linearly increasing compute for every token.

I tested this informally by running identical summarization and reasoning prompts across both models and watching memory and CPU footprints on WaveSpeed. GLM-5 triggered higher peak memory when a request used many experts simultaneously, but average compute per token dropped on longer-context runs. The result felt familiar: better “deep thinking” at scale, without paying for it on short blurbs.

What caught me off guard was how routing patterns show themselves in failure modes. With GLM-4.7, mistakes felt uniform, a bit blunt, predictable. With GLM-5, errors were more varied and sometimes oddly specific: a response might nail one part of a prompt and miss another, which I attributed to expert specialization. That meant prompts that split tasks into explicit steps tended to get steadier results.

Benchmark improvements (SWE-bench, AIME, BrowseComp)

Benchmarks tell part of the story. GLM-5 improves across a few public suites compared to GLM-4.7. In my runs (Jan 2026), GLM-5 showed measurable gains on SWE-bench for code understanding tasks and on AIME for multi-step reasoning. BrowseComp, meant to stress retrieval and up-to-date browsing, also favored GLM-5 on longer chained queries.

Those gains weren’t uniform. For short, well-formed prompts GLM-4.7 was often within a hair’s breadth. Where GLM-5 pulled ahead was in tasks that demanded deeper context aggregation or pragmatic reasoning across multiple facts. In other words, it’s a steadier thinker when the job is complex, and only marginally different when the job is simple.

Speed and latency comparison on WaveSpeed

I ran a small latency sweep on WaveSpeed across three payload sizes: 50 tokens, 300 tokens, and 1,200 tokens. Each test was repeated 20 times during the week of January 12–18, 2026 to smooth out network noise.

50 tokens: GLM-4.7 median latency ~120 ms: GLM-5 median latency ~150 ms.
300 tokens: GLM-4.7 median latency ~420 ms: GLM-5 median latency ~450 ms.
1,200 tokens: GLM-4.7 median latency ~1,800 ms: GLM-5 median latency ~1,650 ms.

Two patterns stood out. First, GLM-5 tends to add a small fixed overhead on short responses, likely routing and expert selection bookkeeping. Second, on long outputs GLM-5 often finishes faster per token because the MoE routing reduces effective compute for sustained sequences.

For real-time UIs or chat widgets where round-trip times on short messages matter, that short-response overhead is visible. For batch generation, summarization, or multi-paragraph content, GLM-5 often saved time overall.

A practical note: WaveSpeed offered both standard and high-concurrency endpoints. The relative differences above were stable across endpoints, but the absolute latencies changed: high-concurrency endpoints narrowed the short-response gap slightly. Your mileage will vary with region and load.

Cost per token — when the upgrade pays for itself

Cost is the quiet decider. I looked at the token pricing WaveSpeed quoted during my tests (January 2026) and calculated cost per useful token: not just generated tokens but the tokens you keep after editing and verification.

GLM-5 is pricier per token than GLM-4.7. The math becomes interesting when GLM-5 reduces human editing time or reduces the number of model calls. Here are scenarios where the upgrade often pays off:

Long-form drafting: If GLM-5 reduces iterations (I saw this in three of five drafting sessions), you make fewer total tokens and save time even at a higher per-token price.
Complex reasoning or synthesis: When a single GLM-5 pass does what two GLM-4.7 passes required, it’s cost-effective.
Teams with higher labor rates: If the person polishing outputs costs more than the token delta, favor GLM-5.

When GLM-5 doesn’t pay: tiny micro-tasks (short labels, simple paraphrases) where GLM-4.7 gives acceptable quality and latency matters. There’s also a middle ground, you can mix models within workflows: use GLM-4.7 for quick drafts and GLM-5 for final synthesis.

I tracked one mini-project: an 800-word article iterated twice on GLM-4.7 and once on GLM-5. Accounting for tokens and 30 minutes of editor time saved, GLM-5 was slightly cheaper overall. That was a small sample, but it aligned with what I’d guessed: GLM-5’s premium pays off when it meaningfully reduces steps.

When to stay on GLM-4.7

Latency-sensitive apps

If your app needs snappy replies for short messages, live chat, autosuggest, interactive UIs, GLM-4.7 still feels better. The extra fixed overhead in GLM-5 adds up when the useful payload is small. I swapped a small search-suggestion widget between models and users noticed the lag at the margin.

Budget constraints

If you’re running high-volume, low-complexity workloads (tagging, simple classification, short paraphrases), GLM-4.7 is the pragmatic choice. The smaller per-token cost and predictable behavior matter more than marginal quality wins. I’d keep GLM-4.7 in a production path for these cases and only route complex queries to GLM-5.

Migration checklist for WaveSpeed users

I migrated a single service last month and kept notes. If you’re considering the switch, these are the steps I’d take.

Baseline metrics (1–2 days): record latency distributions for 3 payload sizes, cost per token, and error/timeout rates on GLM-4.7.
Shadow traffic (1 week): run GLM-5 in parallel for a subset of traffic without returning results to users. Compare accuracy, hallucination patterns, and average edit distance on outputs.
Prompt tuning (a few iterations): because MoE specialization changes behavior, make prompts explicit about step boundaries. I found prompting with numbered steps reduced odd, focused expert errors.
Fallback plan: keep a fast GLM-4.7 route for latency-sensitive paths. Carry out a simple router that switches models by token length or task type.
Cost guardrails: set soft quotas and monitor token spend closely for the first month. GLM-5’s routing can increase peak usage unpredictably.
User testing: show both variants to real users when possible. Metrics are useful, but a human noticing that drafts need less editing was the clearest signal for me.

If you use WaveSpeed’s high-concurrency endpoints, re-test under that configuration: the latency profile changes enough that routing rules might too.

FAQ — backward compatibility, prompt changes

Will my GLM-4.7 prompts work unchanged on GLM-5?

A: Mostly yes, but expect differences. What used to be implicit often needs to be explicit. I had to add short “step” markers and examples in a few prompts to get consistent multi-part outputs.

Are model outputs backward compatible for automated pipelines?

A: Not guaranteed. If you parse model output with brittle rules, test thoroughly. GLM-5’s richer and sometimes more fragmented answers can break simple parsers.

Should I retrain fine-tuned adapters or custom layers?

A: If you have fine-tuned components tied closely to GLM-4.7 logits, plan on re-tuning. I found task-level prompts needed fewer changes than full adapter layers, but that may vary.

Any changes to safety or hallucination profiles?

A: GLM-5 reduced certain hallucination types in my fact-checking runs, but introduced more selective confident errors, statements that read authoritative but were wrong about niche facts. Keep verification steps for high-stakes outputs.

How long before I should switch?

A: If your workflows are heavy on synthesis and editing, try GLM-5 now in a controlled rollout. If you need pure speed for short interactions or have a tight budget, keep GLM-4.7 for the low-level paths and experiment with GLM-5 for higher-value tasks.

A parting note: I don’t expect GLM-5 to be a neat replacement that solves every problem. What it did for me was make some steps feel fewer, fewer edits, fewer passes, a steadier final draft. That small change matters over time. I’m still keeping a few latency-sensitive endpoints on GLM-4.7, and I suspect that’s a pattern many teams will mirror. What I’m curious about next is how expert routing patterns evolve with more training data: for now, the upgrade feels like a measured push forward, not a dramatic leap.