MELongCat 2.0 Open vs 
Gemini 3.1 Pro Preview Paid
LongCat 2.0 vs Gemini 3.1 Pro Preview compared — price per token, context window, multimodality, openness and which to choose. Can the open-source model replace the paid one? Full 2026 breakdown.
Gemini 3.1 Pro Preview — full profile ›
Prices & specs refreshed from live data · olud.ai
Open-model prices = cheapest provider via OpenRouter; official maker rates may be higher.
$1.2 /M outputNo per-token fees if you self-host
$12 /M outputManaged API (no infra to run)
LongCat 2.0 vs Gemini 3.1 Pro Preview specs
| Spec | LongCat 2.0 | Gemini 3.1 Pro Preview | Winner |
|---|---|---|---|
| Maker | Meituan | – | |
| Type | Open-weight | Proprietary | MELongCat 2.0 |
| Context window | 1M tokens | 1M tokens | MELongCat 2.0 |
| Input price | $0.3/M · free self-host | $2/M | MELongCat 2.0 |
| Output price | $1.2/M · free self-host | $12/M | MELongCat 2.0 |
| Vision / multimodal | No | Yes | |
| Tool / function calling | Yes | Yes | = Tie |
| Self-hostable | Yes | No (API only) | MELongCat 2.0 |
| License | Open weights | Proprietary | MELongCat 2.0 |
Price gap & when to choose each
LongCat 2.0 is ~10× cheaper than Gemini 3.1 Pro Preview on output tokens ($1.2 vs $12 per M tokens).
- You want to self-host or run on your cloud
- You need the longer 1M context window
- You prioritize data privacy & control
- You want the lowest operating costs
- You are building open or reproducible AI
- You want frontier performance through a managed API
- You value reliability & ecosystem
- You don't want to manage infrastructure
Feature comparison
| Capability | LongCat 2.0 | Gemini 3.1 Pro Preview |
|---|---|---|
| Open weights (downloadable) | ✓ | ✗ |
| Self-hostable | ✓ | ✗ |
| Runs fully offline | ✓ | ✗ |
| Vision / multimodal | ✗ | ✓ |
| Tool / function calling | ✓ | ✓ |
| 1M+ context window | ✓ | ✓ |
Benchmarks: LongCat 2.0 vs Gemini 3.1 Pro Preview
Independent benchmark scores measured by Artificial Analysis. Higher is better (except latency).
Benchmark data by Artificial Analysis.
How LongCat 2.0 and Gemini 3.1 Pro Preview score
| Criterion | LongCat 2.0 | Gemini 3.1 Pro Preview |
|---|---|---|
| Cost-efficiency | 4.5 | 3.5 |
| Context window | 5.0 | 5.0 |
| Openness | 5.0 | 1.5 |
| Self-hosting | 5.0 | 1.0 |
| Multimodality | 3.5 | 5.0 |
Scores come from live data — output price (cost), context length, open vs closed weights (openness & self-hosting) and vision/tool support (multimodality). Raw task quality isn't scored here; it depends on your benchmark — see the verdict.
What each model is
LongCat 2.0 Open
LongCat 2.0 is a sparse mixture-of-experts language model from Meituan, with 48B active parameters out of 1.6T total. It is suited for coding, repository-level changes, long-horizon problem solving, and agentic...
Gemini 3.1 Pro Preview Paid
Gemini 3.1 Pro Preview is Google’s frontier reasoning model, delivering enhanced software engineering performance, improved agentic reliability, and more efficient token usage across complex workflows. Building on the multimodal foundation.
Other models in these families
These variants are tracked but not compared here — one page per family keeps the comparison readable.
Frequently asked questions
Is LongCat 2.0 as good as Gemini 3.1 Pro Preview?
LongCat 2.0 is open-weight and competitive on many tasks, but Gemini 3.1 Pro Preview may still lead on the hardest reasoning and agentic work. The gap keeps narrowing — benchmark both on your actual use case before deciding.
Can I run LongCat 2.0 locally?
Yes. LongCat 2.0 has open weights, so you can self-host it on your own GPUs or run it via a low-cost API. Gemini 3.1 Pro Preview is API-only and cannot be self-hosted.
How much cheaper is LongCat 2.0?
LongCat 2.0 costs $1.2/M output vs $12/M for Gemini 3.1 Pro Preview — roughly 10x cheaper via API, and free if you self-host.
LongCat 2.0 vs Gemini 3.1 Pro Preview — which should I pick in 2026?
Choose LongCat 2.0 if you want to self-host, keep your data private and skip per-token fees — it's open-weight and runs on your own hardware. Choose Gemini 3.1 Pro Preview if you want frontier capability through a managed API with zero infrastructure to run.
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