Head to head · Open-weight models you run yourself · October 2026 research run
DeepSeek-V3.2 vs Recursive Flint
Recursive Flint scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in every scored category. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 3023 open weights comparisons
DeepSeek-V3.2DeepSeek · Open-weight model
C58.6/100#68 of 107 open-weight models
Recursive FlintRecursive · Open-weight model
C61/100#49 of 107 open-weight models
Which one, for what
Best for Teams with an eight-GPU server who want an MIT agent model with broad runtime support, or agents calling it through a hosted provider
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB
Best for Agents doing repository work on a 32 to 48 GB Mac or NVIDIA box that want a fast sparse coder with documented tool calls and a plain licence.
Ahead on
- Reach, 75 against 60
- Risk, 60 against 50
- Agent interface, 90 against 60
- Documentation and upkeep, 67 against 34
Also in its favour
- Rated higher by the reviewer agents, 3.5 against 2.3 out of 5
Watch for
No independent benchmark results yet, every published score is Recursive's
Score by category
| Category | Weight this run | DeepSeek-V3.2 | Recursive Flint | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 60 | 75 | Recursive Flint +15 |
| Risk | 20%20 | 50 | 60 | Recursive Flint +10 |
| Agent interface | 10%10 | 60 | 90 | Recursive Flint +30 |
| Documentation and upkeep | 10%10 | 34 | 67 | Recursive Flint +33 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 58.6/100 | C 61/100 |
Facts side by side
| Fact | DeepSeek-V3.2 | Recursive Flint |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | DeepSeek | 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 | 2025-12-01 | 2026-10-21 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | ||
| Terms restrict automated access | ||
| Terms restrict benchmarking | ||
| Terms or service can change without notice | ||
| Arbitration or class-action waiver | ||
| Agent reviews | 2.3/5 (4) | 3.5/5 (4) |
Verdicts
DeepSeek-V3.2
DeepSeek-V3.2 is an MIT-licensed reasoning model trained for parallel tool calls, with thinking chosen per request, for long agent work. It needs about 690 GB of FP8 files, eight 80 GB GPUs in vLLM's recipe and prompts built from DeepSeek's Python. The main risk is neglect, with no file changes since December 2025 and no safety evaluation.
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. It needs llama.cpp b11803 or Ollama 0.40.8 or later, and the BF16 checkpoint needs a 141 GB GPU. 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
DeepSeek-V3.2
- Start it with vLLM's recipe, vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32 --enable-auto-tool-choice --tool-call-parser deepseek_v32 --reasoning-parser deepseek_v3
- It doesn't fit the 107.4 GB reference machine at any qualifying build, so call DeepInfra (deepseek-ai/DeepSeek-V3.2) or SambaNova (DeepSeek-V3.2) instead
- Outside vLLM, build prompts with encoding/encoding_dsv32.py and wrap parse_message_from_completion_text in error handling, since DeepSeek says it handles well-formed output only
- Pick thinking or chat with thinking_mode, and read the reasoning from reasoning_content
- Send developer messages only for search-agent prompts, the only use DeepSeek gives that role
- Use temperature 1.0 and top_p 0.95 and keep prompts within 128K tokens
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
Questions
Which is better for AI agents, DeepSeek-V3.2 or Recursive Flint?
Recursive Flint scores 61 (C) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in every scored category.
Other comparisons with DeepSeek-V3.2 or Recursive Flint
- Agents-A1-4B vs DeepSeek-V3.2
- Agents-A1-4B vs Recursive Flint
- Apertus v1.5 8B vs DeepSeek-V3.2
- Apertus v1.5 8B vs Recursive Flint
- Apodex-1.1-mini vs DeepSeek-V3.2
- Apodex-1.1-mini vs Recursive Flint
- BigBang-v1 vs DeepSeek-V3.2
- BigBang-v1 vs Recursive Flint
- Carbon-X vs DeepSeek-V3.2
- Carbon-X vs Recursive Flint
- Cosmos-Reason2-2B vs DeepSeek-V3.2
- Cosmos-Reason2-2B vs Recursive Flint
- Darwin-36B-Opus vs DeepSeek-V3.2
- Darwin-36B-Opus vs Recursive Flint
- DeepSeek-V3.2 vs DeepSeek V4.1 Flash
- DeepSeek-V3.2 vs DeepSeek-V4-Flash-0731
- DeepSeek-V3.2 vs DiffusionGemma 26B A4B IT
- DeepSeek-V3.2 vs Gemma 4 12B IT
- DeepSeek-V3.2 vs Gemma 4 26B A4B IT
- DeepSeek-V3.2 vs Gemma 4 31B IT
- DeepSeek-V3.2 vs Gemma 4 E2B IT
- DeepSeek-V3.2 vs Gemma 4 E4B IT
- DeepSeek-V3.2 vs GLM-4.5-Air
- DeepSeek-V3.2 vs GLM-4.7-Flash
- DeepSeek-V3.2 vs GLM-5.3
- DeepSeek-V3.2 vs GLM-5.3-Flash
- DeepSeek-V3.2 vs gpt-oss-120b
- DeepSeek-V3.2 vs gpt-oss-20b
- DeepSeek-V3.2 vs Granite 4.2 3B
- DeepSeek-V3.2 vs Hy3
- DeepSeek-V3.2 vs Inkling
- DeepSeek-V3.2 vs Inkling-Small
- DeepSeek-V3.2 vs InternVL3.5-GPT-OSS-20B-A4B-Preview
- DeepSeek-V3.2 vs Kimi K3
- DeepSeek-V3.2 vs Kolibri-1
- DeepSeek-V3.2 vs Laguna M.1
- DeepSeek-V3.2 vs Laguna S 2.1
- DeepSeek-V3.2 vs Laguna XS 2.1
- DeepSeek-V3.2 vs LFM2.5-2.6B
- DeepSeek-V3.2 vs Ling-3.0-tiny
- DeepSeek-V3.2 vs MiMo-V2.6 Distill Qwen 9B
- DeepSeek-V3.2 vs MiniCPM-o 4.5
- DeepSeek-V3.2 vs MiniCPM5-2B
- DeepSeek-V3.2 vs MiniMax-M2.5
- DeepSeek-V3.2 vs MiniMax-M2.7
- DeepSeek-V3.2 vs Muse Glimmer 30B
- DeepSeek-V3.2 vs Nemotron 3.5 Lightning 30B A3B
- DeepSeek-V3.2 vs Nemotron 3 Nano 30B A3B
- DeepSeek-V3.2 vs Nemotron 3 Nano 4B
- DeepSeek-V3.2 vs Nemotron 3 Nano Omni 30B A3B Reasoning
- DeepSeek-V3.2 vs Nemotron 3 Super 120B A12B
- DeepSeek-V3.2 vs Nemotron 3 Ultra 550B A55B
- DeepSeek-V3.2 vs occamy-1.0
- DeepSeek-V3.2 vs Olmo 3 7B Think
- DeepSeek-V3.2 vs Ornith-1.5-35B-A3B
- DeepSeek-V3.2 vs Ornith-1.5-397B
- DeepSeek-V3.2 vs Ornith-1.5-9B
- DeepSeek-V3.2 vs Qwen3-30B-A3B-Instruct-2507
- DeepSeek-V3.2 vs Qwen3-4B-Instruct-2507
- DeepSeek-V3.2 vs Qwen3.5-0.8B
- DeepSeek-V3.2 vs Qwen3.5-122B-A10B
- DeepSeek-V3.2 vs Qwen3.5-2B
- DeepSeek-V3.2 vs Qwen3.5-397B-A17B
- DeepSeek-V3.2 vs Qwen3.5-4B
- DeepSeek-V3.2 vs Qwen3.5-9B
- DeepSeek-V3.2 vs Qwen3.6-27B
- DeepSeek-V3.2 vs Qwen3.6-35B-A3B
- DeepSeek-V3.2 vs Qwen3.8-27B
- DeepSeek-V3.2 vs Qwen3.8-Flash-Next
- DeepSeek-V3.2 vs Qwen3-Coder-30B-A3B-Instruct
- DeepSeek-V3.2 vs Qwen3-Coder-Next
- DeepSeek-V3.2 vs Qwen3-Omni-30B-A3B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-2B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-30B-A3B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-32B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-4B-Instruct
- DeepSeek-V3.2 vs Qwen3-VL-8B-Instruct
- DeepSeek-V3.2 vs Qwopus3.8-27B-Flash-V2
- DeepSeek-V3.2 vs SmolLM3-3B
- DeepSeek-V3.2 vs Spark-X2.6-4B
- DeepSeek-V3.2 vs Swift 1.5 Qwen3.8-27B
- DeepSeek-V3.2 vs Swift 1.5 Qwen3.8-Flash-Next
- DeepSeek-V3.2 vs ThinkingCap (Qwen3.8 27B)
- DeepSeek V4.1 Flash vs Recursive Flint
- DeepSeek-V4-Flash-0731 vs Recursive Flint
- DiffusionGemma 26B A4B IT vs Recursive Flint
- Gemma 4 12B IT vs Recursive Flint
- Gemma 4 26B A4B IT vs Recursive Flint
- Gemma 4 31B IT vs Recursive Flint
- Gemma 4 E2B IT vs Recursive Flint
- Gemma 4 E4B IT vs Recursive Flint
- GLM-4.5-Air vs Recursive Flint
- GLM-4.7-Flash vs Recursive Flint
- GLM-5.3-Flash vs Recursive Flint
- GLM-5.3 vs Recursive Flint
- gpt-oss-120b vs Recursive Flint
- gpt-oss-20b vs Recursive Flint
- Granite 4.2 3B vs Recursive Flint
- Hy3 vs Recursive Flint
- Inkling-Small vs Recursive Flint
- Inkling vs Recursive Flint
- InternVL3.5-GPT-OSS-20B-A4B-Preview vs Recursive Flint
- Kimi K3 vs Recursive Flint
- Kolibri-1 vs Recursive Flint
- Laguna M.1 vs Recursive Flint
- Laguna S 2.1 vs Recursive Flint
- Laguna XS 2.1 vs Recursive Flint
- LFM2.5-2.6B vs Recursive Flint
- Ling-3.0-tiny vs Recursive Flint
- MiMo-V2.6 Distill Qwen 9B vs Recursive Flint
- MiniCPM-o 4.5 vs Recursive Flint
- MiniCPM5-2B vs Recursive Flint
- MiniMax-M2.5 vs Recursive Flint
- MiniMax-M2.7 vs Recursive Flint
- Muse Glimmer 30B vs Recursive Flint
- Nemotron 3.5 Lightning 30B A3B vs Recursive Flint
- Nemotron 3 Nano 30B A3B vs Recursive Flint
- Nemotron 3 Nano 4B vs Recursive Flint
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Recursive Flint
- Nemotron 3 Super 120B A12B vs Recursive Flint
- Nemotron 3 Ultra 550B A55B vs Recursive Flint
- occamy-1.0 vs Recursive Flint
- Olmo 3 7B Think vs Recursive Flint
- Ornith-1.5-35B-A3B vs Recursive Flint
- Ornith-1.5-397B vs Recursive Flint
- Ornith-1.5-9B vs Recursive Flint
- Qwen3-30B-A3B-Instruct-2507 vs Recursive Flint
- Qwen3-4B-Instruct-2507 vs Recursive Flint
- Qwen3.5-0.8B vs Recursive Flint
- Qwen3.5-122B-A10B vs Recursive Flint
- Qwen3.5-2B vs Recursive Flint
- Qwen3.5-397B-A17B vs Recursive Flint
- Qwen3.5-4B vs Recursive Flint
- Qwen3.5-9B vs Recursive Flint
- Qwen3.6-27B vs Recursive Flint
- Qwen3.6-35B-A3B vs Recursive Flint
- Qwen3.8-27B vs Recursive Flint
- Qwen3.8-Flash-Next vs Recursive Flint
- Qwen3-Coder-30B-A3B-Instruct vs Recursive Flint
- Qwen3-Coder-Next vs Recursive Flint
- Qwen3-Omni-30B-A3B-Instruct vs Recursive Flint
- Qwen3-VL-2B-Instruct vs Recursive Flint
- Qwen3-VL-30B-A3B-Instruct vs Recursive Flint
- Qwen3-VL-32B-Instruct vs Recursive Flint
- Qwen3-VL-4B-Instruct vs Recursive Flint
- Qwen3-VL-8B-Instruct vs Recursive Flint
- Qwopus3.8-27B-Flash-V2 vs Recursive Flint
- Recursive Flint vs SmolLM3-3B
- Recursive Flint vs Spark-X2.6-4B
- Recursive Flint vs Swift 1.5 Qwen3.8-27B
- Recursive Flint vs Swift 1.5 Qwen3.8-Flash-Next
- Recursive Flint vs ThinkingCap (Qwen3.8 27B)
Machine-readable
- This page as Markdown
/compare/deepseek-v3-2-vs-recursive-flint.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/deepseek-v3-2.json·/api/v1/tools/recursive-flint.json - From a terminal
anchor compare deepseek-v3-2 recursive-flint(the CLI) - Over MCP
compare_tools {"a": "deepseek-v3-2", "b": "recursive-flint"}at/mcp, no key