ERNIE 5.0 vs LongCat-2.5
Baidu · China | Meituan · China · Updated June 2026
Quick verdict
Pick ERNIE 5.0 for baidu's flagship omni-modal model — text, image and video understanding or particularly strong on chinese-language reasoning tasks. Pick LongCat-2.5 for native multimodal upgrade to longcat-2.0 - 1.6t total parameters, about 48b active per token or limited-time api price of $0.30/$1.20 per million tokens (cached input $0.006). On a tight budget at scale, LongCat-2.5 is the value pick.
ERNIE 5.0 (Baidu) and LongCat-2.5 (Meituan) are two of the models people most often weigh against each other in 2026. 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. LongCat-2.5 is meituan's November 2, 2026 natively multimodal successor to LongCat-2.0 - 1.6T parameters and 1M context at $0.30/$1.20, with open weights planned but undated. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
- ▸Price: LongCat-2.5 is about 2× cheaper on input ($0.3/$1.2 per 1M tokens vs $0.6/$2.1 per 1M tokens) — meaningful once you are processing millions of tokens a month.
- ▸Context window: LongCat-2.5 holds 7.8× more — 1M tokens (~1,500 pages) vs 128K (~192 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
- ▸Recency: LongCat-2.5 is the newer model by about 9 months (released November 2, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | ERNIE 5.0 | LongCat-2.5 |
|---|---|---|
| Provider | Baidu (China) | Meituan (China) |
| Released | January 22, 2026 | November 2, 2026 |
| Context window | 128K (~192 pages) | 1M tokens (~1,500 pages) |
| Price (in/out) | $0.6/$2.1 per 1M tokens | $0.3/$1.2 per 1M tokens |
| Open weight? | No — API only | No — API only |
| Modalities | text, image, video, code | text, image, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Baidu's flagship omni-modal model — text, image and video understanding
ERNIE 5.0
ERNIE 5.0 lists baidu's flagship omni-modal model — text, image and video understanding among its strengths; LongCat-2.5 does not.
Particularly strong on Chinese-language reasoning tasks
ERNIE 5.0
ERNIE 5.0 lists particularly strong on Chinese-language reasoning tasks among its strengths; LongCat-2.5 does not.
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; LongCat-2.5 does not.
Native multimodal upgrade to LongCat-2.0 - 1.6T total parameters, about 48B active per token
LongCat-2.5
Its 1M tokens window holds about 7.8× more than ERNIE 5.0's 128K in a single prompt.
Limited-time API price of $0.30/$1.20 per million tokens (cached input $0.006)
LongCat-2.5
At $0.3/$1.2 per 1M tokens it undercuts ERNIE 5.0 ($0.6/$2.1 per 1M tokens), and that gap compounds at volume.
Meituan-reported Terminal-Bench v2.1 of 88.4, up from 70.8 for LongCat-2.0
LongCat-2.5
Meituan's November 2, 2026 natively multimodal successor to LongCat-2.0 - 1.6T parameters and 1M context at $0.30/$1.20, with open weights planned but undated — and it runs cheaper at $0.3/$1.2 per 1M tokens.
Lowest cost at scale
LongCat-2.5
At $0.3/$1.2 per 1M tokens, it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
LongCat-2.5
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
→ LongCat-2.5
At $0.3/$1.2 per 1M tokens it undercuts ERNIE 5.0, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ LongCat-2.5
Larger 1M tokens window fits more in one prompt.
Anyone whose priority is baidu's flagship omni-modal model — text, image and video understanding
→ ERNIE 5.0
It is specifically built for that.
Anyone whose priority is native multimodal upgrade to longcat-2.0 - 1.6t total parameters, about 48b active per token
→ LongCat-2.5
That is its strongest area.
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 are real: 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.
LongCat-2.5: where it fits
Meituan's November 2, 2026 natively multimodal successor to LongCat-2.0 - 1.6T parameters and 1M context at $0.30/$1.20, with open weights planned but undated. Released November 2, 2026 by Meituan, it is built for native multimodal upgrade to LongCat-2.0 - 1.6T total parameters, about 48B active per token, limited-time API price of $0.30/$1.20 per million tokens (cached input $0.006), meituan-reported Terminal-Bench v2.1 of 88.4, up from 70.8 for LongCat-2.0, and openAI- and Anthropic-compatible API endpoints with a 1M-token context window.
Its trade-offs: aPI-only for now - GitHub and Hugging Face releases are planned but undated, and the license is unconfirmed (LongCat-2.0 is MIT), the $0.30/$1.20 rate is labeled limited-time, and Meituan says prices are subject to change, and benchmarks are Meituan's own, and sources found no independent score at launch. At $0.3 in / $1.2 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
ERNIE 5.0 and LongCat-2.5 overlap enough that the right pick depends on your specific job. LongCat-2.5 costs less per token; LongCat-2.5 holds the larger context; and each leads in its own area — ERNIE 5.0 for baidu's flagship omni-modal model — text, image and video understanding, LongCat-2.5 for native multimodal upgrade to longcat-2.0 - 1.6t total parameters, about 48b active per token. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both ERNIE 5.0 and LongCat-2.5 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 pricingFrequently asked questions
Is ERNIE 5.0 or LongCat-2.5 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, ERNIE 5.0 leans toward baidu's flagship omni-modal model — text, image and video understanding while LongCat-2.5 leans toward native multimodal upgrade to longcat-2.0 - 1.6t total parameters, about 48b active per token, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, ERNIE 5.0 or LongCat-2.5?
LongCat-2.5 is cheaper — $0.6/$2.1 per 1M tokens vs $0.3/$1.2 per 1M tokens, roughly 2× apart on input.
Which has the bigger context window?
LongCat-2.5 — 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 ERNIE 5.0 and LongCat-2.5 together?
Yes — a multi-model platform like LumiChats gives you ERNIE 5.0, LongCat-2.5 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, ERNIE 5.0 or LongCat-2.5?
LongCat-2.5 — released November 2, 2026, about 9 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.