# DeepSeek V4.1 Flash vs Ling-3.0-tiny > DeepSeek V4.1 Flash scores 59 (C) to Ling-3.0-tiny's 55 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny - Markdown: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny.md (~6,400 tokens) - Slim: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny.json (this page as data, same URL with Accept: application/json) - Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key) - Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data) - Updated: 2026-10-21 DeepSeek V4.1 Flash scores 59 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 2 of 5 scored categories. Ling-3.0-tiny leads on reach and documentation and upkeep. Both do open-weight models you run yourself. - DeepSeek V4.1 Flash: grade C, 59/100, rank #63 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v4-1-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json - Ling-3.0-tiny: grade C, 55/100, rank #81 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/ling-3-0-tiny.md · JSON https://www.anchorterminal.com/api/v1/tools/ling-3-0-tiny.json - Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md - All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md ## Which one, for what ### DeepSeek V4.1 Flash (C) Good for: Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback. Ahead on: - Capability, 69 against 31 - Agent interface, 75 against 60 Watch for: 510.3 GB of files and no smaller build anywhere, so it won't fit the 107.4 GB reference machine ### Ling-3.0-tiny (C) Good for: Agents on a laptop or small GPU that want a fast local model for tool calls and short reasoning, under MIT Ahead on: - Reach, 90 against 50 - Documentation and upkeep, 56 against 48 Watch for: No hosted provider today, and Ollama support is only an unmerged pull request ## Score by category | Category | Weight | DeepSeek V4.1 Flash | Ling-3.0-tiny | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 69 | 31 | DeepSeek V4.1 Flash +38 | | Reach | 25% (25 this run) | 50 | 90 | Ling-3.0-tiny +40 | | Risk | 20% (20 this run) | 50 | 50 | even | | Agent interface | 10% (10 this run) | 75 | 60 | DeepSeek V4.1 Flash +15 | | Documentation and upkeep | 10% (10 this run) | 48 | 56 | Ling-3.0-tiny +8 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **59 · C** | **55 · C** | | ## Facts side by side | Fact | DeepSeek V4.1 Flash | Ling-3.0-tiny | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | DeepSeek | inclusionAI (Ant Group) | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | MIT | MIT | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-10-17 | 2026-08-31 | | 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 | 2.8/5 (4) | 2.8/5 (4) | ## Verdicts **DeepSeek V4.1 Flash.** A large multimodal agent model with a 1M-token context, documented tool calls, JSON Schema output and a reasoning budget, under MIT. It needs about 612 GB of memory across several GPUs in vLLM or SGLang. The main risks are prompts built from DeepSeek's code rather than a template, no published safety results and no answers in its repositories. **Ling-3.0-tiny.** A small, quick hybrid reasoning cousin for local agent work, tool calls and coding, under MIT. It runs in about 6 GB from inclusionAI's 4-bit GGUF in released Transformers, vLLM, SGLang and llama.cpp, with no host. The main risks are a card that doesn't match the files in places, the vLLM route running repository code, and no model safety results. ## Before you call either ### DeepSeek V4.1 Flash 1. DeepSeek's reference run is its inference folder, convert.py with --model-parallel 8 --expert-dtype fp4, then torchrun --nproc-per-node 8 generate.py --ckpt-path "$SAVE_PATH" --config config.json --interactive 2. In vLLM 0.32.0 the parsers are named deepseek_v41 for both --tool-call-parser and --reasoning-parser. That's vLLM's naming, DeepSeek doesn't document a vLLM command 3. Build prompts with encoding.py or pip install deepseek-recipe, not the September repository's chat_template.jinja, which an open pull request says trails the 16 October encoding 4. Tool calls come back in a DSML calls block with a space before calls, invoke and parameter, unlike V4 5. Set reasoning_effort to 1 to 100, or low, high or max (50, 75, 100), in thinking mode only. Default is 75 6. Sample at temperature 1.0 and top_p 1.0 and allow 256K or more output tokens, as the card recommends 7. Without the hardware, call DeepSeek's API as deepseek-flash, which serves this exact update ### Ling-3.0-tiny 1. Start it with `llama-server -hf inclusionAI/Ling-3.0-tiny-GGUF:Q4_K_M --jinja -c 32768`, which needs llama.cpp b11772 or a recent release with bailingmoe3 2. With vLLM 0.32.0, `vllm serve inclusionAI/Ling-3.0-tiny --trust-remote-code --enable-prefix-caching --mamba-cache-mode align --enable-auto-tool-choice --tool-call-parser ling3 --reasoning-parser ling3`, which runs the repository's config code 3. With SGLang 0.5.22, its cookbook uses `--reasoning-parser deepseek-r1 --tool-call-parser glm45` for this model, and drop the card's NEXTN flags since tiny has no MTP layer 4. Thinking is on by default, pass `chat_template_kwargs` with `enable_thinking` false for short tasks 5. Sampling per the card is temperature 1.0, top_p 0.95 and top_k 20 6. The template keeps earlier reasoning in the prompt, which grows long agent sessions 7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented 8. For a guardrail, inclusionAI's Ling-3.0-tiny-singprobe scores intent, unsafe output and hallucination per token, but only through SGLang and vLLM integration branches ## Questions ### Which is better for AI agents, DeepSeek V4.1 Flash or Ling-3.0-tiny? DeepSeek V4.1 Flash scores 59 (C) on agent readiness against Ling-3.0-tiny's 55 (C), and leads in 2 of 5 scored categories. Ling-3.0-tiny leads on reach and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "deepseek-v4-1-flash", "b": "ling-3-0-tiny"}`. 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[Ling-3.0-tiny vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-thinkingcap-qwen3-8-27b.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). 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