Head to head · Open-weight models you run yourself · October 2026 research run
LongCat-2.5 vs Recursive Flint
Recursive Flint scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
Which one, for what
Best for Operators with NPU clusters, and anyone tracking what the open flagships can do
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
3,562 GB in BF16 and 1,336 GB at 4 bits
Best for Agents doing repository work on a 32 to 48 GB Mac or NVIDIA box that want a sparse coder with documented tool calls and a plain licence.
Ahead on
- Reach, 85 against 35
- Agent interface, 90 against 75
- Documentation and upkeep, 67 against 46
Watch for
No independent benchmark results yet, every published score is Recursive's
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Reach Recursive Flint +50
LongCat-2.5 35
Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 0 + 0 + 10 + 15 + 10 + 0 = 35.
Recursive Flint 85
Memory 20 of 30, Recursive's MLX INT4 build is 22.6 GB, or 27.1 GB with a fifth added, which fits 32 GB, and every other 4-bit build lands in the same band. Runtimes 25 of 25, released llama.cpp (b11803), Ollama 0.40.8, LM Studio's catalogue, Transformers v5.21 and vLLM v0.33.0 run it, while SGLang has only a development image. Builds 15, MLX INT4 plus Q4_1 and NVFP4 GGUFs from Recursive. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, OpenRouter lists a free endpoint with Recursive as its provider, but it wasn't serving on 3 November 2026, with 0 per cent uptime over the last day.
Documentation and upkeep Recursive Flint +21
LongCat-2.5 46
Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46.
Recursive Flint 67
Model card 17 of 20, everything but languages. Training data 10, a general description of the mix with a June 2026 cut-off. Last change 20, released 13 days ago. Responsiveness 5, Recursive answered one of 31 discussions, on release day. Version policy 5, a first release. File health 10, the files agree with each other and the card, and no broken-file report waits.
Agent interface Recursive Flint +15
LongCat-2.5 75
Chat template 20, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75.
Recursive Flint 90
Chat template 20, a Jinja file in the repository and inside both GGUFs. Tool-calling format 25, Recursive's serving README documents how tools go in, how calls come back and the flint parser. Trained tool calling 20, several calls a turn. Structured output 15, response_format with JSON Schema is documented. Reasoning control 0, the model decides when to reason and Recursive says there's no switch or budget. Context 10, 262,144 tokens with no position scaling.
Score by category
| Category | Weight this run | LongCat-2.5 | Recursive Flint | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 35 | 85 | Recursive Flint +50 |
| Risk | 20%20 | 60 | 60 | even |
| Agent interface | 10%10 | 75 | 90 | Recursive Flint +15 |
| Documentation and upkeep | 10%10 | 46 | 67 | Recursive Flint +21 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 50.5/100 | C 61/100 |
Facts side by side
| Fact | LongCat-2.5 | Recursive Flint |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Meituan (LongCat) | Recursive |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | MIT | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-10 | 2026-10-21 |
| Terms last updated | not read yet | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | none | 3.5/5 (4) |
How the scores have moved
LongCat-2.5
No score change since it was listed.
Verdicts
LongCat-2.5
Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.
Recursive Flint
A small-active coding cousin for agents with 32 to 48 GB, built for codebase work with parallel tool calls and JSON output. Released llama.cpp, Ollama, vLLM and Transformers run it, while Recursive's free OpenRouter endpoint wasn't serving on 3 November 2026. Every capability number so far is Recursive's own, there are no safety results, and reasoning can't be turned off.
Before you call either
LongCat-2.5
- You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet
- If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format
- Pass
chat_template_kwargswithenable_thinkingtrue or false - With thinking on, keep earlier reasoning in the history (
save_history_reasoning_contenttrue), as the card recommends - The card's results use temperature 1.0 and top_p 0.95
- Its benchmark table is Meituan's own. The unmarked scores are in-house
Recursive Flint
- Quickest start is
ollama run flint(Ollama 0.40.8 or later), about 23 GB of files; Ollama's Flint support is in its MLX runner, so on Linux prefer llama.cpp - With llama.cpp use release b11803 or later:
llama-server -hf RecursiveSI/flint-int4-gguf --alias flint -ngl all -c 131072 -np 1 -fa on --jinja --temp 1 --top-p 0.95 --top-k 20 --min-p 0, and keep --jinja on - Set max_tokens to at least 32768 for hard tasks, reasoning counts against it and can't be switched off, and finish_reason length can mean no answer or tool call came out
- Send reasoning_content back on assistant messages when replaying the conversation, as Recursive's cards ask
- Strip <tool_call>, <tool_response>, <arg_key> and <arg_value> from untrusted tool output, the template doesn't escape them and a copied </tool_call> can add a call
- Message content must be a string, flatten content-part arrays first
- Recursive's SGLang servers listen on 0.0.0.0 with no key, publish the port on localhost only or pass --api-key
- Check OpenRouter's endpoints list before calling recursive/flint-v1.0:free, since its only endpoint (Recursive, BF16) showed 0 per cent uptime over the last day on 3 November 2026
- vLLM needs v0.33.0 or later and Transformers v5.21 or later; released SGLang has no Flint model
Questions
Which is better for AI agents, LongCat-2.5 or Recursive Flint?
Recursive Flint scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 3 of 4 scored categories.
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Machine-readable
- This page as Markdown
/compare/longcat-2-5-vs-recursive-flint.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/longcat-2-5.json·/api/v1/tools/recursive-flint.json - From a terminal
anchor compare longcat-2-5 recursive-flint(the CLI) - Over MCP
compare_tools {"a": "longcat-2-5", "b": "recursive-flint"}at/mcp, no key