LongCat-2.5 vs Mistral Large 4
Meituan · China | Mistral AI · France · Updated June 2026
Quick verdict
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). Pick Mistral Large 4 for mistral's largest model to date — about 1t total parameters, 49b active per token (52b with embeddings and output layers), plus a 1.6b-parameter vision encoder or trained from scratch on 3,800 nvidia grace blackwell gpus in mistral's own european data centers, across 160+ languages. Choose Mistral Large 4 if you need self-hosting or data privacy; LongCat-2.5 if you want a managed API.
LongCat-2.5 (Meituan, China) and Mistral Large 4 (Mistral AI, France) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. 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. Mistral Large 4 is mistral's 'Le Chonk' — a 1T-parameter, natively multimodal MoE model previewed October 6, 2026, with open weights published October 29 and API pricing of $0.70/$2.10. They diverge most on price and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
- ▸Price: LongCat-2.5 is about 2.3× cheaper on input ($0.3/$1.2 per 1M tokens vs $0.7/$2.1 per 1M tokens) — meaningful once you are processing millions of tokens a month.
- ▸Context window: both advertise 1M tokens (~1,500 pages). Tie on paper — test on your own long inputs, since usable recall varies by model.
- ▸Recency: LongCat-2.5 is the newer model by about 27 days (released November 2, 2026), usually meaning fresher training data and capabilities.
- ▸Ecosystem: this is a China-vs-France matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
| Spec | LongCat-2.5 | Mistral Large 4 |
|---|---|---|
| Provider | Meituan (China) | Mistral AI (France) |
| Released | November 2, 2026 | October 6, 2026 |
| Context window | 1M tokens (~1,500 pages) | 1M tokens (~1,500 pages) |
| Price (in/out) | $0.3/$1.2 per 1M tokens | $0.7/$2.1 per 1M tokens |
| Open weight? | No — API only | Yes — self-hostable |
| Modalities | text, image, code | text, image, code |
| SWE-Bench Verified | Not published | Not published |
| MRCR v2 @ 1M | Not published | Not published |
Who wins what
Native multimodal upgrade to LongCat-2.0 - 1.6T total parameters, about 48B active per token
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.
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 Mistral Large 4 ($0.7/$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
Mistral Large 4 is comparatively weak here — scores 61.7% on DeepSWE v1.1, trailing DeepSeek V4.1 Flash (74.2%) and Kimi K3 (68.0%) on that coding benchmark
Mistral's largest model to date — about 1T total parameters, 49B active per token (52B with embeddings and output layers), plus a 1.6B-parameter vision encoder
Mistral Large 4
Mistral's 'Le Chonk' — a 1T-parameter, natively multimodal MoE model previewed October 6, 2026, with open weights published October 29 and API pricing of $0.70/$2.10 — and its weights are open while LongCat-2.5 is API-only.
Trained from scratch on 3,800 Nvidia Grace Blackwell GPUs in Mistral's own European data centers, across 160+ languages
Mistral Large 4
Mistral Large 4 lists trained from scratch on 3,800 Nvidia Grace Blackwell GPUs in Mistral's own European data centers, across 160+ languages among its strengths; LongCat-2.5 does not.
Hybrid instruct/reasoning design — answers quickly by default or switches into step-by-step reasoning
Mistral Large 4
Mistral Large 4 lists hybrid instruct/reasoning design — answers quickly by default or switches into step-by-step reasoning among its strengths; LongCat-2.5 does not.
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.
Which should you pick?
A cost-sensitive startup shipping high volume
→ LongCat-2.5
At $0.3/$1.2 per 1M tokens it undercuts Mistral Large 4, and on millions of tokens that margin decides the monthly bill.
A team with data-privacy or self-hosting needs
→ Mistral Large 4
Open weights let you run it on your own hardware; LongCat-2.5 is API-only.
Anyone whose priority is native multimodal upgrade to longcat-2.0 - 1.6t total parameters, about 48b active per token
→ LongCat-2.5
It is specifically built for that.
Anyone whose priority is mistral's largest model to date — about 1t total parameters, 49b active per token (52b with embeddings and output layers), plus a 1.6b-parameter vision encoder
→ Mistral Large 4
That is its strongest area.
An enterprise with regional data-residency rules
→ Mistral Large 4 or LongCat-2.5
Origin (China vs France) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
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 are real: 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.
Mistral Large 4: where it fits
Mistral's 'Le Chonk' — a 1T-parameter, natively multimodal MoE model previewed October 6, 2026, with open weights published October 29 and API pricing of $0.70/$2.10. Released October 6, 2026 by Mistral AI, it is built for mistral's largest model to date — about 1T total parameters, 49B active per token (52B with embeddings and output layers), plus a 1.6B-parameter vision encoder, trained from scratch on 3,800 Nvidia Grace Blackwell GPUs in Mistral's own European data centers, across 160+ languages, hybrid instruct/reasoning design — answers quickly by default or switches into step-by-step reasoning, and open weights on Hugging Face since October 29, 2026, with an NVFP4 checkpoint for Blackwell GPUs, making it among the largest open-weight models available.
Its trade-offs: launched as a public preview — Mistral's launch post said the model was still improving, mistral Ecosystem Open-Weight License is a modified MIT license: selling hosted access to third parties needs a separate commercial license, self-hosting a 1T-parameter MoE needs datacenter-class GPUs, and scores 61.7% on DeepSWE v1.1, trailing DeepSeek V4.1 Flash (74.2%) and Kimi K3 (68.0%) on that coding benchmark. At $0.7 in / $2.1 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. Mistral Large 4 gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. LongCat-2.5 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 LongCat-2.5 and Mistral Large 4 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 LongCat-2.5 or Mistral Large 4 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, LongCat-2.5 leans toward native multimodal upgrade to longcat-2.0 - 1.6t total parameters, about 48b active per token while Mistral Large 4 leans toward mistral's largest model to date — about 1t total parameters, 49b active per token (52b with embeddings and output layers), plus a 1.6b-parameter vision encoder, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, LongCat-2.5 or Mistral Large 4?
Mistral Large 4 is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while LongCat-2.5 is API-metered at $0.3/$1.2 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?
Both advertise 1M tokens (~1,500 pages). Remember advertised ≠ usable: recall typically degrades before the ceiling.
Can I use both LongCat-2.5 and Mistral Large 4 together?
Yes — a multi-model platform like LumiChats gives you LongCat-2.5, Mistral Large 4 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, LongCat-2.5 or Mistral Large 4?
LongCat-2.5 — released November 2, 2026, about 27 days after Mistral Large 4.
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.