# DeepSeek-V3.2 vs DeepSeek-V4-Flash-0731 > DeepSeek-V4-Flash-0731 scores 62.4 (B) to DeepSeek-V3.2's 58.6 (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-v3-2-vs-deepseek-v4-flash-0731 - Markdown: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-flash-0731.md (~6,450 tokens) - Slim: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-flash-0731.min.md (~430 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-flash-0731.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-Flash-0731 scores 62.4 (B) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 3 of 5 scored categories. Both do open-weight models you run yourself. - DeepSeek-V3.2: grade C, 58.6/100, rank #65 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v3-2.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v3-2.json - DeepSeek-V4-Flash-0731: grade B, 62.4/100, rank #28 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v4-flash-0731.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v4-flash-0731.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-V3.2 (C) Good 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 Watch for: 689.5 GB of FP8 files and no smaller build from DeepSeek or a runtime library, far beyond 107.4 GB ### DeepSeek-V4-Flash-0731 (B) Good for: Agents or teams with a multi-GPU server, or a provider account, who want DeepSeek's V4 agent model under MIT with a 1M context Ahead on: - Reach, 70 against 60 - Agent interface, 75 against 60 - Documentation and upkeep, 43 against 34 Watch for: The smallest 4-bit build is 156.4 GB, so it needs about 188 GB and doesn't fit a 128 GB machine ## Score by category | Category | Weight | DeepSeek-V3.2 | DeepSeek-V4-Flash-0731 | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 69 | 66 | DeepSeek-V3.2 +3 | | Reach | 25% (25 this run) | 60 | 70 | DeepSeek-V4-Flash-0731 +10 | | Risk | 20% (20 this run) | 50 | 50 | even | | Agent interface | 10% (10 this run) | 60 | 75 | DeepSeek-V4-Flash-0731 +15 | | Documentation and upkeep | 10% (10 this run) | 34 | 43 | DeepSeek-V4-Flash-0731 +9 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **58.6 · C** | **62.4 · B** | | ## Facts side by side | Fact | DeepSeek-V3.2 | DeepSeek-V4-Flash-0731 | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | DeepSeek | DeepSeek | | 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 | 2025-12-01 | 2026-07-31 | | 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 | 2.3/5 (4) | 2.5/5 (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. **DeepSeek-V4-Flash-0731.** A long-context agent cousin under MIT with documented tool calls, three reasoning effort levels and support in every major runtime but Ollama. It needs about 190 to 200 GB of memory, though six inference providers serve it. The main risks are prompts built from DeepSeek's Python, no published safety results and no answers in its repository. ## Before you call either ### DeepSeek-V3.2 1. 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 2. 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 3. 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 4. Pick thinking or chat with thinking_mode, and read the reasoning from reasoning_content 5. Send developer messages only for search-agent prompts, the only use DeepSeek gives that role 6. Use temperature 1.0 and top_p 0.95 and keep prompts within 128K tokens ### DeepSeek-V4-Flash-0731 1. vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 --trust-remote-code --kv-cache-dtype fp8 --block-size 256 --data-parallel-size 4 --enable-expert-parallel --moe-backend deep_gemm_mega_moe --attention-config '{"use_fp4_indexer_cache": true}' --speculative-config '{"method":"dspark","num_speculative_tokens":7,"draft_sample_method":"greedy"}' --tokenizer-mode deepseek_v4 --tool-call-parser deepseek_v4 --enable-auto-tool-choice --reasoning-parser deepseek_v4 (the card's 4x GB300 example plus the parser flags from vLLM's recipe) 2. Build prompts with encoding_dsv4.py, deepseek-recipe's V4 encoder or vLLM's --tokenizer-mode deepseek_v4, since the repository has no Jinja template 3. Tool calls come back in a DSML tool_calls block with invoke elements, string parameters carry string="true" and JSON values string="false" 4. Set temperature 1.0 and top_p 0.95 for agent work, and allow up to 384K output tokens at reasoning effort high or max 5. Without the hardware, call it through a Hugging Face inference provider (Together, DeepInfra, Novita, Baseten, Scaleway, Featherless), not DeepSeek's API, where deepseek-v4-flash now means V4.1 Flash 6. LM Studio's library build is a 156.4 GB MXFP4 GGUF and LM Studio states 156 GB minimum system memory ## Questions ### Which is better for AI agents, DeepSeek-V3.2 or DeepSeek-V4-Flash-0731? DeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against DeepSeek-V3.2's 58.6 (C), and leads in 3 of 5 scored categories. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-flash-0731.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-flash-0731.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "deepseek-v3-2", "b": "deepseek-v4-flash-0731"}`. 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