# DeepSeek-V4-Flash-0731 vs GLM-5.3-Flash > GLM-5.3-Flash scores 65.1 (B) to DeepSeek-V4-Flash-0731's 62.4 (B) 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-glm-5-3-flash - Markdown: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3-flash.md (~6,500 tokens) - Slim: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3-flash.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3-flash.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 GLM-5.3-Flash scores 65.1 (B) on agent readiness against DeepSeek-V4-Flash-0731's 62.4 (B), and leads in 3 of 5 scored categories. DeepSeek-V4-Flash-0731 leads on reach and agent interface. 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 - GLM-5.3-Flash: grade B, 65.1/100, rank #16 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/glm-5-3-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/glm-5-3-flash.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: - Reach, 70 against 60 - Agent interface, 75 against 70 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 ### GLM-5.3-Flash (B) Good for: Teams with a multi-GPU server who want an MIT-licensed multimodal agent model with a 1M-token context, or agents that call it through a hosted provider Ahead on: - Capability, 75 against 66 - Risk, 55 against 50 - Documentation and upkeep, 58 against 43 Watch for: Z.ai's smallest build is 328.4 GB in FP8, and no Ollama or LM Studio local build exists ## Score by category | Category | Weight | DeepSeek-V4-Flash-0731 | GLM-5.3-Flash | Edge | | --- | --- | --- | --- | --- | | Capability | 35% (35 this run) | 66 | 75 | GLM-5.3-Flash +9 | | Reach | 25% (25 this run) | 70 | 60 | DeepSeek-V4-Flash-0731 +10 | | Risk | 20% (20 this run) | 50 | 55 | GLM-5.3-Flash +5 | | Agent interface | 10% (10 this run) | 75 | 70 | DeepSeek-V4-Flash-0731 +5 | | Documentation and upkeep | 10% (10 this run) | 43 | 58 | GLM-5.3-Flash +15 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **62.4 · B** | **65.1 · B** | | ## Facts side by side | Fact | DeepSeek-V4-Flash-0731 | GLM-5.3-Flash | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | DeepSeek | Z.ai | | 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 | 2026-07-31 | 2026-08-26 | | 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) | 2.8/5 (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. **GLM-5.3-Flash.** An MIT-licensed multimodal agent and coding model with a 1M-token context and several tool calls per turn, served by vLLM, SGLang, llama.cpp and Transformers. Z.ai's smallest build is 328 GB in FP8, so it needs a multi-GPU server. The main risk is that nothing on safety or prompt injection is published and there is no way to report a flaw. ## 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 ### GLM-5.3-Flash 1. Start it with `vllm serve zai-org/GLM-5.3-Flash --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser glm47 --reasoning-parser glm47` (vLLM 0.32.0 or later, about 386 GB of GPU memory per vLLM's recipe) 2. Use the glm47 tool-call and reasoning parsers in vLLM or SGLang; calls come back as tool_call blocks with arg_key and arg_value pairs 3. Pass `chat_template_kwargs: {"reasoning_effort": "low"}` for short tasks; anything other than low or high falls back to max 4. Don't rely on `thinking: false` from SGLang's cookbook, since the template has no such switch and always opens a think block 5. Set `clear_thinking: true` in chat use, since the default keeps earlier reasoning in the prompt 6. On a 128 GB machine only third-party low-bit builds come near fitting, none of them from Z.ai ## Questions ### Which is better for AI agents, DeepSeek-V4-Flash-0731 or GLM-5.3-Flash? GLM-5.3-Flash scores 65.1 (B) on agent readiness against DeepSeek-V4-Flash-0731's 62.4 (B), and leads in 3 of 5 scored categories. DeepSeek-V4-Flash-0731 leads on reach and agent interface. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-glm-5-3-flash.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "deepseek-v4-flash-0731", "b": "glm-5-3-flash"}`. 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