LongCat-2.0 and Gemini 3.1 Pro: Open-Weight vs Closed Frontier Flagship Compared
A source-based comparison of LongCat-2.0 (1.6T MoE, MIT License, $0.30/M input, self-hostable) and Google Gemini 3.1 Pro (preview flagship, natively multimodal, $2/M input with tiered long-context pricing). Covers architecture, pricing, licensing, context, modalities, and deployment.
Independent third-party resource. Not affiliated with or endorsed by LongCat, Meituan, DeepSeek, or any other publisher discussed on this page.
Published: 2026-08-11 · Author: LongCat Community Hub editorial team
This is an independent, source-based comparison of LongCat-2.0 (Meituan) and Gemini 3.1 Pro (Google DeepMind). It compares documented architecture, pricing, licensing, context windows, modalities, and deployment options. It does not report performance as a verdict — for publisher-reported scores, see the individual model pages and benchmark index linked below. Every claim on this page is sourced from the linked publisher documentation.
| Dimension | LongCat-2.0 | Gemini 3.1 Pro |
|---|---|---|
| Developer | Meituan (LongCat team) | Google DeepMind |
| Model class | Open-weight flagship | Frontier Pro-tier flagship (preview status) |
| Release date | 2026 (2.0 generation) | February 19, 2026 |
| Total parameters | 1.6T (MoE) | Undisclosed (proprietary) |
| Active parameters | ~48B per token (33B—56B dynamic) | Undisclosed |
| Architecture | ScMoE + LSA sparse attention + N-gram Embedding | Proprietary (undisclosed) |
| Context window | 1M tokens (native) | 1M tokens input (1,048,576) |
| Max output | 128K tokens | 65,536 tokens |
| Modalities | Text | Text, image, video, audio, PDF (native) |
| API pricing (per 1M tokens) | $0.30 input / $1.20 output (OpenRouter) | $2.00 input / $12.00 output (≤200K context); $4.00 / $18.00 above 200K |
| Cache pricing | Free (cache hits not billed) | Cached input $0.20/M (90% off) |
| Cost ratio (relative, standard context) | 1× | ~6.7× input, ~10× output |
| License | MIT License (open-source weights) | Proprietary (closed model) |
| Thinking mode | Dynamic (MOPD distilled, adaptive) | Thinking supported (native) |
| Self-hosted deployment | Yes — SGLang, vLLM, Transformers, NPU path | No (Gemini API, Vertex AI, AI Studio only) |
Two Distribution Models for Two Capability Ceilings
Gemini 3.1 Pro is Google DeepMind's current Pro-tier flagship, released February 19, 2026 as a preview. It is a natively multimodal model — accepting text, image, video, audio, and PDF input — with a 1M-token context window, 64K-token output, and Google's tiered pricing that starts at $2/M input. Its parameter count and architecture are undisclosed.
LongCat-2.0 is a fully documented open-weight model: 1.6T MoE, ~48B active per token, MIT license, trained on domestic AI ASICs, and priced at $0.30/$1.20 per M tokens through OpenRouter. As with the Claude and GPT comparisons on this site, the meaningful contrast here is not only capability but distribution: auditable open weights that can be self-hosted, versus a closed frontier API whose feature set — here, native multimodality and deep Google ecosystem integration — sits behind the vendor's endpoint.
Pricing: A ~7× Gap, Widened by Long-Context Tiers
At standard context (≤200K), Gemini 3.1 Pro is about 6.7× more expensive on input ($2.00 vs $0.30 per M tokens) and 10× more expensive on output ($12.00 vs $1.20 per M tokens) than LongCat-2.0 on OpenRouter. Google's tiered pricing raises the input rate to $4.00/M and output to $18.00/M once a request exceeds 200K tokens — so the gap widens on precisely the long-context workloads where a 1M window is most used. Cached input at $0.20/M softens this but still exceeds LongCat-2.0's unbilled cache hits.
Self-hosting LongCat-2.0 removes per-token fees entirely at infrastructure cost, widening the gap further for high-volume workloads. These are metered headline rates; real cost depends on cache-hit rates, context length, and infrastructure decisions.
Modality: Text vs Native Multimodality
The clearest feature difference is modality. Gemini 3.1 Pro is natively multimodal across text, image, video, audio, and PDF, making it suited to mixed-media workloads such as video transcripts, scanned documents, and audio analysis. LongCat-2.0 is documented as a text model; multimodal capabilities sit in separate LongCat-family products (image and video generation) rather than in the flagship LLM.
For teams whose pipeline is text-only — document processing, coding agents, API orchestration — this difference may be irrelevant. For teams analyzing mixed media, Gemini 3.1 Pro offers an integrated multimodal path that LongCat-2.0 does not provide in the same model.
Context, Output Limits, and Ecosystem
Both models offer a 1M-token input context, but they differ on output: LongCat-2.0 documents 128K-token output versus Gemini 3.1 Pro's 64K. For long-generation tasks such as large multi-file code edits or extended reports, the output ceiling can matter. Gemini 3.1 Pro's 64K is nonetheless large for most interactive workloads.
Gemini 3.1 Pro is tightly integrated into Google's stack — Gemini API, Vertex AI, AI Studio, Workspace, and search grounding — which favors teams already on Google infrastructure. LongCat-2.0 is available through the LongCat API, OpenRouter, and self-hosted deployment via SGLang, vLLM, or Transformers (plus an NPU inference path), with OpenAI- and Anthropic- compatible endpoints for drop-in integration and no lock-in to a vendor endpoint.
Preview Status and Version Velocity
Gemini 3.1 Pro shipped as a preview and has remained so through mid-2026; Google has repeatedly deferred a GA successor (3.5 Pro) that was announced in May 2026 but had not generally released as of August 2026. Preview status matters for teams that require contractual SLA guarantees or frozen model behavior. LongCat-2.0 is an open-weight release whose weights, once downloaded, remain runnable regardless of vendor roadmap decisions.
This is a snapshot as of August 11, 2026. Google's frontier lineup changes frequently, so verify current availability, pricing, and status on Google's official documentation before relying on the above for production decisions.
This comparison is based on publicly available publisher documentation accessed on 2026-08-11. LongCat-2.0 specifications are from the publisher blog, GitHub repository, and OpenRouter. Gemini 3.1 Pro specifications are from the Google AI for Developers model card, the DeepMind model card, and Google's pricing documentation.
This is a feature-level comparison, not a performance evaluation. This site has not independently tested either model. Benchmark scores published by either vendor have not been independently verified. Google's claims about Gemini 3.1 Pro's benchmark positions are vendor-reported.
Pricing and model availability are current as of the access date and may change. Gemini 3.1 Pro's preview status and Google's frontier roadmap mean specs can shift. Verify current terms, availability, and rates on each vendor's official documentation before deployment.
Related pages
- LongCat-2.0 model profile
Full technical brief covering architecture, training, benchmarks, and deployment options.
- LongCat-2.0 vs GPT-5.6 Luna comparison
The other US frontier comparison on this site, for cross-checking the pricing picture.
- LongCat-2.0 vs Claude Fable 5 comparison
A companion open-weight vs closed frontier comparison against Anthropic.
- LongCat-2.0 Publisher-Reported Benchmark Scores
The benchmark data cited in this comparison with source attribution.
Related comparisons in this series
- LongCat-2.0 and GPT-5.5: Documented Architecture, Access, and Cost Differences
A source-based comparison of LongCat-2.0 (1.6T MoE, MIT License, domestic hardware) and GPT-5.5 (frontier closed model, proprietary). Covers architecture, pricing, context, and deployment — not a performance review.
- LongCat-2.0 and GPT-5.6 Luna: Open-Weight vs Closed Economy Tier Compared
A source-based comparison of LongCat-2.0 (1.6T MoE, MIT License, $0.30/M input) and GPT-5.6 Luna (OpenAI's economy tier, proprietary, $0.20/M input after an 80% price cut on July 30, 2026). Covers architecture, pricing, licensing, context, and deployment.
- LongCat-2.0 and Claude Fable 5: Open-Weight vs Frontier Closed Model Compared
A source-based comparison of LongCat-2.0 (1.6T MoE, MIT License, $0.30/M input, self-hostable) and Claude Fable 5 (Anthropic's Mythos-class frontier model, $10/M input, API-only). Covers architecture, pricing, licensing, context, and the June 2026 export-control suspension.
Sources
- LongCat-2.0 Publisher Blog Post
Publisher documentationAccessed 2026-08-11
Publisher announcement detailing LongCat-2.0 architecture (ScMoE, LSA, N-gram Embedding, MOPD), 1.6T parameters, domestic-ASIC training, and MIT License.
- LongCat-2.0 GitHub Repository
Primary sourceAccessed 2026-08-11
Model code, weights, MIT License terms, and deployment instructions.
- OpenRouter — LongCat-2.0
Third-partyAccessed 2026-08-11
$0.30/M input, $1.20/M output, 1.05M context.
- Gemini 3.1 Pro — Google AI for Developers model card
Publisher documentationPublished 2026-02-19Accessed 2026-08-11
Official model card: gemini-3.1-pro-preview, 1,048,576-token input and 65,536-token output limits, multimodal input (text, image, video, audio, PDF), thinking and caching supported.
- Gemini 3.1 Pro model card — Google DeepMind
Publisher documentationAccessed 2026-08-11
DeepMind model card with publisher-reported benchmark results including GPQA Diamond, ARC-AGI-2, and SWE-Bench Verified.
- Gemini API pricing
Publisher documentationAccessed 2026-08-11
Official pricing: $2.00/M input and $12.00/M output for contexts up to 200K tokens; $4.00/M input and $18.00/M output above 200K; cached input at $0.20/M (90% discount).
Independent third-party disclosure
This page is published by an independent third-party site. It is not affiliated with, endorsed by, sponsored by, or operated by Meituan, LongCat, or any of their affiliates. The content summarizes publicly-available primary documentation and does not represent the views of any referenced organization.
Last reviewed: 2026-08-11