# DeepSeek-V4-Flash-0731 vs Qwopus3.8-27B-Flash-V2 > DeepSeek-V4-Flash-0731 scores 62.4 (B) to Qwopus3.8-27B-Flash-V2's 61 (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-flash-0731-vs-qwopus3-8-27b-flash-v2 - Markdown: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwopus3-8-27b-flash-v2.md (~6,900 tokens) - Slim: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwopus3-8-27b-flash-v2.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwopus3-8-27b-flash-v2.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 Qwopus3.8-27B-Flash-V2's 61 (C), and leads in 1 of 4 scored categories. Qwopus3.8-27B-Flash-V2 leads on reach and documentation and upkeep. Both do open-weight models you run yourself. - 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 - Qwopus3.8-27B-Flash-V2: grade C, 61/100, rank #46 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwopus3-8-27b-flash-v2.md · JSON https://www.anchorterminal.com/api/v1/tools/qwopus3-8-27b-flash-v2.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-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: - Agent interface, 75 against 60 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 ### Qwopus3.8-27B-Flash-V2 (C) Good for: Hobbyist and research agents that want a faster-finishing Qwen3.8-27B for local coding loops on a 24 GB GPU. Ahead on: - Reach, 85 against 70 - Documentation and upkeep, 56 against 43 Watch for: Published from a personal account, with no organisation, security contact or safety evaluation ## Score by category | Category | Weight | DeepSeek-V4-Flash-0731 | Qwopus3.8-27B-Flash-V2 | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 70 | 85 | Qwopus3.8-27B-Flash-V2 +15 | | Risk | 20% (20 this run) | 50 | 50 | even | | Agent interface | 10% (10 this run) | 75 | 60 | DeepSeek-V4-Flash-0731 +15 | | Documentation and upkeep | 10% (10 this run) | 43 | 56 | Qwopus3.8-27B-Flash-V2 +13 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **62.4 · B** | **61 · C** | | ## Facts side by side | Fact | DeepSeek-V4-Flash-0731 | Qwopus3.8-27B-Flash-V2 | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | DeepSeek | Jackrong | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | MIT | Apache-2.0 (metadata only, no LICENSE file); built on Qwen3.8-27B under Apache 2.0 | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2026-07-31 | 2026-09-21 | | 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 | 2.5/5 (4) | 2.5/5 (4) | ## Verdicts **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. **Qwopus3.8-27B-Flash-V2.** A Qwen3.8-27B cousin tuned to think less per turn in long agent loops, with an MTP head for speculative decoding in llama.cpp. It fits 24 GB with Jackrong's 4-bit GGUF. The risks are one person's experimental release with no safety work, training traces taken from Claude and GPT, and a tool format that differs from Qwen's. ## Before you call either ### 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 ### Qwopus3.8-27B-Flash-V2 1. Quickest start is `llama-server -hf Jackrong/Qwopus3.8-27B-Flash-V2-GGUF:Q4_K_M --jinja`, about 17 GB, and add mmproj-F32.gguf for images 2. The card's tests used Q5_K_M with the bundled MTP head and `--spec-type draft-mtp` in llama.cpp 3. The template asks for tool calls as JSON inside tool_call tags, so use a Hermes-style parser (vLLM's hermes), not Qwen3.8's qwen3_coder 4. For coding the card recommends enable_thinking true, reasoning_effort xhigh and temperature 0.3, which you must pass yourself, since generation_config.json says 1.0 5. reasoning_effort takes xhigh (default), medium or low, and any other value makes the template raise an error 6. Earlier reasoning stays in the context unless preserve_thinking is false 7. Cap max_tokens, since a report says it loops more than V1 ## Questions ### Which is better for AI agents, DeepSeek-V4-Flash-0731 or Qwopus3.8-27B-Flash-V2? DeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against Qwopus3.8-27B-Flash-V2's 61 (C), and leads in 1 of 4 scored categories. Qwopus3.8-27B-Flash-V2 leads on reach and documentation and upkeep. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwopus3-8-27b-flash-v2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwopus3-8-27b-flash-v2.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "deepseek-v4-flash-0731", "b": "qwopus3-8-27b-flash-v2"}`. 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