Quick answer · 12 November 2026
● Price: Fable 5.2 is $8 in / $40 out per million. GLM-5.3 is $1.40 in / $4.40 out. 5.7x on input, 9x on output.
● Pick Fable if: you send images. GLM-5.3 is text-only input, which rules it out of a large class of work on its own.
● Pick GLM-5.3 if: the work is text and code, the volume is real, or you need to run the weights yourself.
● On benchmarks: they do not line up. The two labs publish almost entirely different suites. Anyone showing you a clean head-to-head table is comparing different tests.
● GLM 5.2 vs 5.3: identical price, same base model, 5.3 wins every published benchmark. There is no reason to run 5.2.
Side by side
Every figure read off the vendor page on 12 November 2026 — Anthropic for Fable, the Z.ai developer docs for GLM. Blanks are blanks: where a vendor does not publish a number, this table says so rather than borrowing one from a roundup.
|
Claude Fable 5.2 |
GLM-5.3 |
| Input / 1M tokens |
$8.00 |
$1.40 |
| Output / 1M tokens |
$40.00 |
$4.40 |
| Cached input / 1M |
not published |
$0.26 |
| Fast / high-speed tier |
$16 in / $80 out |
off-peak calls use 50% of points |
| Context window |
not published |
1M tokens |
| Max output |
not published |
128K tokens |
| Image input |
Yes |
No — text only |
| Weights |
Closed |
Open, on Hugging Face |
| Reasoning control |
Effort levels incl. High and Xhigh |
Always on, cannot disable. low / high / max |
| Released |
9 November 2026 |
August 2026 |
| API model ID |
claude-fable-5-2 |
glm-5.3 |
The price gap is the headline, and it is bigger than it looks
Input is 5.7 times. Output is 9 times. Output is where agentic work spends, so the gap you feel in a monthly bill sits closer to the 9 than the 5.7.
A job burning 10M input and 3M output tokens a month costs $200 on Fable 5.2 and $27.20 on GLM-5.3. On Fable Fast at $16 and $80 it is $400. Those are list prices multiplied out, not estimates, and they ignore caching — GLM publishes cached input at $0.26 against $1.40, so a cache-friendly workload widens the gap again.
The question this forces: is Fable 5.2 seven to nine times better? On the evidence either lab publishes, nobody can tell you — and that is not a dodge, it is the next section.
Why there is no honest benchmark table here
We wanted to put the scores side by side. They do not go side by side, and it is worth explaining why, because every other comparison of these two models will show you a table that quietly pretends otherwise.
Anthropic publishes Fable 5.2 on FrontierCode 2.0, FrontierSWE v2, AutomationBench, Terminal-Bench-Science 0.1, WANDR, OSWorld 2.1, RoboDojo Sim, AA-Briefcase v1.1, the Remote Labor Index, Humanity’s Last Exam, Chartography and BenchCAD 2.1. Z.ai publishes GLM-5.3 on Terminal-Bench 2.1 and 3.0, DeepSWE v1.1, NL2Repo, ProgramBench, FrontierSWE, SWE-Marathon, PostTrainBench, CyberGym, ExploitGym, ExploitBench, Toolathlon, AutomationBench v1.0.6, Agents’ Last Exam, HLE with tools and GDPval-AA.
Two suites overlap by name. Neither overlaps cleanly:
| Benchmark |
Fable 5.2 |
GLM-5.3 |
Comparable? |
| Humanity’s Last Exam, with tools |
74.2% |
62.5 |
Closest thing to a fair run |
| AutomationBench |
51.4% |
48.2 (v1.0.6) |
Different versions |
| FrontierSWE |
69.9% (v2) |
78.1 |
Different versions |
| Terminal-Bench-Science |
74.6% (0.1) |
9.5 |
Different harness — do not read this as a gap |
That last row is the clearest warning. Fable scores 74.6% on Terminal-Bench-Science 0.1 and GLM-5.3 shows 9.5 on a Terminal-Bench Science entry. A 65-point gap between two frontier models on the same named test is not a capability difference, it is two different setups. Published as a straight comparison it would be a lie with a citation attached.
What to take from this: on the single pairing that comes closest to a fair run, Humanity’s Last Exam with tools, Fable 5.2 leads 74.2% to 62.5. That is one data point, not a verdict, and it is nowhere near enough on its own to justify nine times the output price. Decide on the things below instead, which are facts rather than scores.
The four things that actually decide this
1. Image input — and this one is binary
GLM-5.3 takes text only. Z.ai’s own model page says so. If your work involves screenshots, documents, charts, diagrams or UI, GLM-5.3 is not a candidate at any price and the rest of this comparison is academic. Fable 5.2 handles vision — Anthropic publishes it at 73.4% on Chartography for chart reading and 89.3% on OSWorld 2.1 for computer use, both of which require seeing.
This single line eliminates more buyers than every benchmark on the page.
2. Open weights
GLM-5.3 ships its weights on Hugging Face. You can run it on your own hardware, in your own network, with no per-token bill and no data leaving your perimeter. For regulated work, air-gapped environments or anyone who has been burned by a model being deprecated underneath them, that is not a feature, it is the whole decision. Fable 5.2 is API-only.
Worth reading alongside: Z.ai held the GLM-5.3 weights for two weeks before release, a hold it had never applied before.
3. Reasoning you cannot switch off
GLM-5.3 has reasoning always on and no way to disable it. Effort is low, high or max, defaulting to max. That is fine for hard problems and wasteful for simple ones — you pay thinking tokens on a classification call that never needed them. Fable exposes effort levels too, and Anthropic publishes the difference: FrontierCode 2.0 scores 45.3% at High and 49.1% at Xhigh. Four points of capability for a materially larger token spend, which is a dial worth having.
4. Context
GLM-5.3 publishes a 1M token context window and 128K max output. Anthropic does not publish a context figure for Fable 5.2 on its announcement page. If a million tokens of context decides your architecture, one of these models has told you the number and the other has not.
The split neither lab is advertising
These two models landed within three months of each other and their labs took opposite positions on the same question: who should be able to use a model that is good at finding vulnerabilities.
Z.ai made cyber capability the headline of the upgrade. GLM-5.3 scores 84.5% on CyberGym against 77.2% for 5.2, and 54.4% on ExploitBench against 24.4%. On ExploitGym it completes 105 tasks in two hours and 130 in six, where 5.2 managed 29 and 39. Z.ai reports 2,436 vulnerabilities found across 269 real projects, 1,097 of them medium-to-high severity. Then it published the weights.
Anthropic split its model in two over the same capability. Fable 5.2 and Mythos 5.2 share one underlying model. Fable is available to everyone with stronger cybersecurity and biology safeguards. Mythos goes only to vetted organisations through a verification programme, and the cyber numbers are published under Mythos rather than Fable: 76.3% on CyScenarioBench and 89.6% on CVE Rediscovery.
Same capability class, same quarter. One lab gated it behind vetting; the other open-sourced it. Which one you think is right probably decides which of these models you are comfortable building on, and that is a procurement question rather than a benchmark one. We took the Anthropic half apart in Fable 5.2 vs Mythos 5.2.
If you came here for GLM 5.2
Short answer: use 5.3. GLM-5.2 was announced in June 2026 with 753B parameters, a 1M context window and MIT-licensed weights, and Z.ai prices both models identically — $1.40 input, $0.26 cached, $4.40 output per million. Same money.
They share a base model. Every gain in 5.3 comes from post-training, and it wins every benchmark Z.ai publishes for both:
| Benchmark |
GLM-5.2 |
GLM-5.3 |
| Terminal-Bench 2.1 |
81.0 |
88.2 |
| Terminal-Bench 3.0 |
4.6 |
28.3 |
| DeepSWE v1.1 |
46.2 |
66.9 |
| FrontierSWE |
67.5 |
78.1 |
| SWE-Marathon v1.1 |
19.4 |
42.5 |
| AutomationBench v1.0.6 |
26.2 |
48.2 |
| HLE with tools |
54.7 |
62.5 |
| CyberGym |
77.2% |
84.5% |
| ExploitBench |
24.4% |
54.4% |
The only reason to stay on 5.2 is that you have already validated it and do not want to requalify. There is no price reason and no capability reason.
Which one, concretely
If your work includes images, PDFs, screenshots or UI
→ Fable 5.2. GLM-5.3 is text-only input. Nothing else matters.
If you are running high-volume text or code agents
→ GLM-5.3. Nine times less on output is not a margin, it is a different business model.
If the data cannot leave your network
→ GLM-5.3, self-hosted. Fable is API-only.
If you need a published context guarantee
→ GLM-5.3 publishes 1M context and 128K max output. Anthropic does not publish a context figure for Fable 5.2.
If you want the dial between cost and effort
→ Fable 5.2. Anthropic publishes what each effort level buys. GLM-5.3 cannot turn reasoning off at all.
If you are doing security research
→ Read the section above before choosing. Both labs are capable here and they have opposite access policies.
If you are already on GLM-5.2
→ Move to 5.3. Same price, same base model, better on every published benchmark.
If you want one model for everything
→ Fable 5.2, and accept the bill. Breadth is what the premium buys.
The split most teams land on: GLM-5.3 for the volume text and code work, Fable 5.2 for anything with an image in it and for the hard reasoning calls. Routing by task rather than standardising on one model is cheaper than either choice alone, and the price gap is wide enough to pay for the routing logic many times over.
Questions people ask
Is GLM-5.3 really nine times cheaper than Claude Fable 5.2?
On output, yes: $4.40 against $40.00 per million tokens, both from the vendors own pricing pages. Input is $1.40 against $8.00, so 5.7 times. Because agentic work is output-heavy, real bills land nearer the 9x figure than the 5.7x one.
Can GLM-5.3 read images?
No. Z.ai documents GLM-5.3 as text-only input. For screenshots, charts, PDFs or anything visual you need Fable 5.2 or another multimodal model. This is the single most common reason teams evaluate GLM and then do not adopt it.
Which is better at coding?
Nobody can answer this honestly from published numbers, because the two labs do not run the same coding benchmarks. GLM-5.3 posts strong results on Terminal-Bench, DeepSWE and SWE-Marathon; Fable 5.2 posts FrontierCode 2.0 and FrontierSWE v2. The only near-comparable pairing is Humanity’s Last Exam with tools, where Fable leads 74.2% to 62.5. Run both on your own repository for a week; it will tell you more than any table.
Should I upgrade from GLM-5.2 to GLM-5.3?
Yes. Identical pricing at $1.40 input and $4.40 output, the same base model, and 5.3 leads on every benchmark Z.ai publishes for both — Terminal-Bench 3.0 goes 4.6 to 28.3, DeepSWE 46.2 to 66.9. The only reason to stay is avoiding requalification.
Can I run GLM-5.3 on my own hardware?
Yes. The weights are published on Hugging Face at 753B parameters in a mixture-of-experts configuration, so this is serious hardware rather than a laptop. But it is possible, and Fable 5.2 is API-only with no self-hosted option at any price.
What is the difference between Claude Fable 5.2 and Mythos 5.2?
They are the same underlying model with different access and safeguards. Fable is available to everyone with stronger cybersecurity and biology safeguards. Mythos is restricted to vetted organisations through a verification programme, and Anthropic publishes the cyber and biology scores under Mythos. Pricing is identical at $8 and $40.
Listings: Claude Fable 5.2 · GLM-5.3. Related: GLM-5.3 vs Kimi K3 · Gemini 4 Carbon vs Fable 5.2.
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