Claude Fable 5.1 vs GLM 5

Anthropic · US  |  Z.ai · China · Updated June 2026

Quick verdict

Pick Claude Fable 5.1 for terminal-bench-science agentic research (52.6%, more than double fable 5's 24.7%) or cursorbench 3.2 agentic coding (73.4%). Pick GLM 5 for agentic planning and long-horizon coding workflows or complex systems design and backend reasoning. Choose GLM 5 if you need self-hosting or data privacy; Claude Fable 5.1 if you want a managed API.

Claude Fable 5.1 (Anthropic, US) and GLM 5 (Z.ai, China) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. Claude Fable 5.1 is anthropic's September 1, 2026 update to its Fable line for coding and knowledge work, keeping the 1M-token context of Fable 5 while cutting cache-read costs and roughly doubling agentic-research benchmark scores. GLM 5 is z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. They diverge most on price, context window and open vs. closed weights — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecClaude Fable 5.1GLM 5
ProviderAnthropic (US) Z.ai (China)
ReleasedSeptember 1, 2026 February 12, 2026
Context window1M tokens (~1,500 pages) 200K (~300 pages)
Price (in/out)$10/$50 per 1M tokens $1/$3.2 per 1M tokens
Open weight?No — API only Yes — self-hostable
Modalitiestext, image text, code
SWE-Bench VerifiedNot published 77.8%
MRCR v2 @ 1MNot published Not published

Who wins what

Terminal-Bench-Science agentic research (52.6%, more than double Fable 5's 24.7%)

Claude Fable 5.1

Anthropic's September 1, 2026 update to its Fable line for coding and knowledge work, keeping the 1M-token context of Fable 5 while cutting cache-read costs and roughly doubling agentic-research benchmark scores — and it carries the larger 1M tokens context.

CursorBench 3.2 agentic coding (73.4%)

Claude Fable 5.1

Anthropic's September 1, 2026 update to its Fable line for coding and knowledge work, keeping the 1M-token context of Fable 5 while cutting cache-read costs and roughly doubling agentic-research benchmark scores — and it is the newer of the two.

Cache-read pricing cut to a quarter of other Claude models

Claude Fable 5.1

Claude Fable 5.1 lists cache-read pricing cut to a quarter of other Claude models among its strengths; GLM 5 does not.

Agentic planning and long-horizon coding workflows

GLM 5

Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding — and it runs cheaper at $1/$3.2 per 1M tokens.

Complex systems design and backend reasoning

GLM 5

Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding — and its weights are open while Claude Fable 5.1 is API-only.

Iterative self-correction on autonomous tasks

GLM 5

GLM 5 lists iterative self-correction on autonomous tasks among its strengths; Claude Fable 5.1 does not.

Lowest cost at scale

GLM 5

At $1/$3.2 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.

Largest single-prompt input

Claude Fable 5.1

Its 1M tokens window is about 5× larger than GLM 5's 200K, fitting roughly 1,500 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

GLM 5

At $1/$3.2 per 1M tokens it undercuts Claude Fable 5.1, and on millions of tokens that margin decides the monthly bill.

Someone analysing very long documents or codebases

Claude Fable 5.1

Larger 1M tokens window fits more in one prompt.

A team with data-privacy or self-hosting needs

GLM 5

Open weights let you run it on your own hardware; Claude Fable 5.1 is API-only.

Anyone whose priority is terminal-bench-science agentic research (52.6%, more than double fable 5's 24.7%)

Claude Fable 5.1

It is specifically built for that.

Anyone whose priority is agentic planning and long-horizon coding workflows

GLM 5

That is its strongest area.

An enterprise with regional data-residency rules

Claude Fable 5.1 or GLM 5

Origin (US vs China) affects where data is processed and which compliance regime applies — check the provider's terms for your region.

Claude Fable 5.1: where it fits

Anthropic's September 1, 2026 update to its Fable line for coding and knowledge work, keeping the 1M-token context of Fable 5 while cutting cache-read costs and roughly doubling agentic-research benchmark scores. Released September 1, 2026 by Anthropic, it is built for terminal-Bench-Science agentic research (52.6%, more than double Fable 5's 24.7%), cursorBench 3.2 agentic coding (73.4%), cache-read pricing cut to a quarter of other Claude models, and long, multistep, document-heavy professional work with 1M-token context.

Its trade-offs are real: anthropic has not published an official SWE-bench Verified score for this model, restricted Mythos 5.1 variant outscores it on some benchmarks due to lighter safeguards (Terminal-Bench 4.0: 60.9% vs 55.8%), and closed weights, no self-hosting option. At $10 in / $50 out per million tokens, it sits in the premium price band.

GLM 5: where it fits

Z.ai's flagship open-weight (MIT) MoE foundation model, engineered for complex systems design and long-horizon agentic coding. Released February 12, 2026 by Z.ai, it is built for agentic planning and long-horizon coding workflows, complex systems design and backend reasoning, iterative self-correction on autonomous tasks, and open weights under the permissive MIT license.

Its trade-offs: 200K context trails 1M-context rivals, and quickly superseded by GLM-5.1 and GLM-5.2. At $1 in / $3.2 out per million tokens, it sits in the budget price band.

The bottom line for this matchup

The defining split here is open vs. closed. GLM 5 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Claude Fable 5.1 gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.

Want both Claude Fable 5.1 and GLM 5 without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.

See pricing

Frequently asked questions

Is Claude Fable 5.1 or GLM 5 better for coding?

Public SWE-Bench figures are not available for Claude Fable 5.1, so the honest test is your own repository — run an identical real bug through both. By design, Claude Fable 5.1 leans toward terminal-bench-science agentic research (52.6%, more than double fable 5's 24.7%) while GLM 5 leans toward agentic planning and long-horizon coding workflows, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Claude Fable 5.1 or GLM 5?

GLM 5 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Claude Fable 5.1 is API-metered at $10/$50 per 1M tokens. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.

Which has the bigger context window?

Claude Fable 5.1 — 1M tokens vs 200K, about 5× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both Claude Fable 5.1 and GLM 5 together?

Yes — a multi-model platform like LumiChats gives you Claude Fable 5.1, GLM 5 and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.

Which is newer, Claude Fable 5.1 or GLM 5?

Claude Fable 5.1 — released September 1, 2026, about 7 months after GLM 5.

Related comparisons

Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.