# DeepSeek-V4-Flash-0731 vs Qwen3-30B-A3B-Instruct-2507

> DeepSeek-V4-Flash-0731 scores 62.4 (B) to Qwen3-30B-A3B-Instruct-2507's 56.6 (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-v4-flash-0731-vs-qwen3-30b-a3b-instruct-2507
- Markdown: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-30b-a3b-instruct-2507.md (~7,100 tokens)
- Slim: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-30b-a3b-instruct-2507.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-30b-a3b-instruct-2507.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 Qwen3-30B-A3B-Instruct-2507's 56.6 (C), and leads in 2 of 5 scored categories. Qwen3-30B-A3B-Instruct-2507 leads on reach and risk. 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
- Qwen3-30B-A3B-Instruct-2507: grade C, 56.6/100, rank #74 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-30b-a3b-instruct-2507.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-30b-a3b-instruct-2507.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:
- Capability, 66 against 37
- Documentation and upkeep, 43 against 26

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

### Qwen3-30B-A3B-Instruct-2507 (C)

Good for: Agents that want a fast, capable instruct model on one 24 GB GPU or a 32 GB Mac, with a plain licence and long context.

Ahead on:
- Reach, 90 against 70
- Risk, 55 against 50

Watch for: No safety results, prompt-injection guidance or security contact


## Score by category

| Category | Weight | DeepSeek-V4-Flash-0731 | Qwen3-30B-A3B-Instruct-2507 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 66 | 37 | DeepSeek-V4-Flash-0731 +29 |
| Reach | 25% (25 this run) | 70 | 90 | Qwen3-30B-A3B-Instruct-2507 +20 |
| Risk | 20% (20 this run) | 50 | 55 | Qwen3-30B-A3B-Instruct-2507 +5 |
| Agent interface | 10% (10 this run) | 75 | 75 | even |
| Documentation and upkeep | 10% (10 this run) | 43 | 26 | DeepSeek-V4-Flash-0731 +17 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **62.4 · B** | **56.6 · C** | |

## Facts side by side

| Fact | DeepSeek-V4-Flash-0731 | Qwen3-30B-A3B-Instruct-2507 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | DeepSeek | Alibaba (Qwen team) |
| 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 | 2026-07-31 | 2025-09-17 |
| 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.

**Qwen3-30B-A3B-Instruct-2507.** A quick general-purpose cousin, with 3.3B of 30.5B parameters active per token, for instruction following, tool calling and long documents without a thinking step. It needs about 21 to 23 GB at 4 bits, and several providers host it. The main risk is neglect, with no file changes in over a year, no answers from Alibaba and no safety results.

## 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

### Qwen3-30B-A3B-Instruct-2507

1. Quickest start is `ollama run qwen3:30b-instruct`, 19 GB. Plain `qwen3:30b` and `qwen3:30b-a3b` are the Thinking-2507 edition, not this one
2. On our reference machine use BF16 with `vllm serve Qwen/Qwen3-30B-A3B-Instruct-2507 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser hermes`, 61 GB of weights fits the GPU share
3. Use the hermes parser in vLLM or qwen25 in SGLang, the template emits JSON inside tool_call tags
4. Don't pass enable_thinking, this edition never thinks and the card says the flag isn't needed
5. Sampling per the card is temperature 0.7, top_p 0.8, top_k 20 and min_p 0, with presence_penalty up to 2 against repetition
6. Leave config.json as it is unless you need more than 262,144 tokens, the 1M mode swaps in config_1m.json and runs one sequence at a time
7. Validate JSON yourself or use the runtime's guided decoding, since structured output isn't documented

## Questions

### Which is better for AI agents, DeepSeek-V4-Flash-0731 or Qwen3-30B-A3B-Instruct-2507?

DeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against Qwen3-30B-A3B-Instruct-2507's 56.6 (C), and leads in 2 of 5 scored categories. Qwen3-30B-A3B-Instruct-2507 leads on reach and risk.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-30b-a3b-instruct-2507.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-30b-a3b-instruct-2507.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "deepseek-v4-flash-0731", "b": "qwen3-30b-a3b-instruct-2507"}`. From a terminal: `anchor compare deepseek-v4-flash-0731 qwen3-30b-a3b-instruct-2507`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepseek-v4-flash-0731.json and https://www.anchorterminal.com/api/v1/tools/qwen3-30b-a3b-instruct-2507.json

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