Claude Fable 5.1 vs ERNIE 5.0

Anthropic · US  |  Baidu · 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 ERNIE 5.0 for baidu's flagship omni-modal model — text, image and video understanding or particularly strong on chinese-language reasoning tasks. On a tight budget at scale, ERNIE 5.0 is the value pick.

Claude Fable 5.1 (Anthropic, US) and ERNIE 5.0 (Baidu, 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. ERNIE 5.0 is baidu's flagship omni-modal ERNIE model — strong Chinese-language and multimodal reasoning at low cost, though it trails the Western frontier on independent benchmarks. They diverge most on price and context window — each quantified below from the models' real specs.

Key differences at a glance

Side-by-side specs

SpecClaude Fable 5.1ERNIE 5.0
ProviderAnthropic (US) Baidu (China)
ReleasedSeptember 1, 2026 January 22, 2026
Context window1M tokens (~1,500 pages) 128K (~192 pages)
Price (in/out)$10/$50 per 1M tokens $0.6/$2.1 per 1M tokens
Open weight?No — API only No — API only
Modalitiestext, image text, image, video, code
SWE-Bench VerifiedNot published Not published
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; ERNIE 5.0 does not.

Baidu's flagship omni-modal model — text, image and video understanding

ERNIE 5.0

Claude Fable 5.1 is comparatively weak here — anthropic has not published an official SWE-bench Verified score for this model

Particularly strong on Chinese-language reasoning tasks

ERNIE 5.0

Baidu's flagship omni-modal ERNIE model — strong Chinese-language and multimodal reasoning at low cost, though it trails the Western frontier on independent benchmarks — and it runs cheaper at $0.6/$2.1 per 1M tokens.

Competitive API pricing (around $0.60/$2.10 per million tokens)

ERNIE 5.0

ERNIE 5.0 lists competitive API pricing (around $0.60/$2.10 per million tokens) among its strengths; Claude Fable 5.1 does not.

Lowest cost at scale

ERNIE 5.0

At $0.6/$2.1 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 7.8× larger than ERNIE 5.0's 128K, fitting roughly 1,500 pages in one prompt.

Which should you pick?

A cost-sensitive startup shipping high volume

ERNIE 5.0

At $0.6/$2.1 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.

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 baidu's flagship omni-modal model — text, image and video understanding

ERNIE 5.0

That is its strongest area.

An enterprise with regional data-residency rules

Claude Fable 5.1 or ERNIE 5.0

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.

ERNIE 5.0: where it fits

Baidu's flagship omni-modal ERNIE model — strong Chinese-language and multimodal reasoning at low cost, though it trails the Western frontier on independent benchmarks. Released January 22, 2026 by Baidu, it is built for baidu's flagship omni-modal model — text, image and video understanding, particularly strong on Chinese-language reasoning tasks, competitive API pricing (around $0.60/$2.10 per million tokens), and backed by a major lab with deep China-market integration.

Its trade-offs: trails the Western frontier on aggregate independent tests (AA Intelligence Index ~22 for the tracked Thinking Preview), parameter and architecture details are vendor-stated and opaque, closed weights on a China-hosted API, and 128K context is smaller than 1M-token rivals. At $0.6 in / $2.1 out per million tokens, it sits in the budget price band.

The bottom line for this matchup

This is less "which is smarter" and more "which ecosystem fits." Claude Fable 5.1 (US) and ERNIE 5.0 (China) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. ERNIE 5.0 is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.

Want both Claude Fable 5.1 and ERNIE 5.0 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 ERNIE 5.0 better for coding?

Public SWE-Bench figures are not available for either model, 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 ERNIE 5.0 leans toward baidu's flagship omni-modal model — text, image and video understanding, and that positioning usually predicts which feels better on your codebase.

Which is cheaper, Claude Fable 5.1 or ERNIE 5.0?

ERNIE 5.0 is cheaper — $10/$50 per 1M tokens vs $0.6/$2.1 per 1M tokens, roughly 17× apart on input.

Which has the bigger context window?

Claude Fable 5.1 — 1M tokens vs 128K, about 7.8× 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 ERNIE 5.0 together?

Yes — a multi-model platform like LumiChats gives you Claude Fable 5.1, ERNIE 5.0 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 ERNIE 5.0?

Claude Fable 5.1 — released September 1, 2026, about 7 months after ERNIE 5.0.

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.