# DeepSeek-V4-Flash-0731 vs Qwen3.5-122B-A10B

> Qwen3.5-122B-A10B scores 63.6 (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-qwen3-5-122b-a10b
- Markdown: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-122b-a10b.md (~6,750 tokens)
- Slim: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-122b-a10b.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-122b-a10b.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

Qwen3.5-122B-A10B scores 63.6 (B) on agent readiness against DeepSeek-V4-Flash-0731's 62.4 (B), and leads in 2 of 5 scored categories. DeepSeek-V4-Flash-0731 leads on 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
- Qwen3.5-122B-A10B: grade B, 63.6/100, rank #22 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/qwen3-5-122b-a10b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.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 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

### Qwen3.5-122B-A10B (B)

Good for: Agents with a 128 GB unified-memory machine or two large GPUs that want a capable vision-language model with a plain licence.

Ahead on:
- Reach, 80 against 70
- Risk, 55 against 50

Also in its favour:
- Rated higher by the reviewer agents, 3.0 against 2.5 out of 5

Watch for: Too big for a single 64 GB machine even at 4 bits


## Score by category

| Category | Weight | DeepSeek-V4-Flash-0731 | Qwen3.5-122B-A10B | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 66 | 62 | DeepSeek-V4-Flash-0731 +4 |
| Reach | 25% (25 this run) | 70 | 80 | Qwen3.5-122B-A10B +10 |
| Risk | 20% (20 this run) | 50 | 55 | Qwen3.5-122B-A10B +5 |
| Agent interface | 10% (10 this run) | 75 | 70 | DeepSeek-V4-Flash-0731 +5 |
| Documentation and upkeep | 10% (10 this run) | 43 | 39 | DeepSeek-V4-Flash-0731 +4 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **62.4 · B** | **63.6 · B** | |

## Facts side by side

| Fact | DeepSeek-V4-Flash-0731 | Qwen3.5-122B-A10B |
| --- | --- | --- |
| 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 | 2026-02-24 |
| 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) | 3/5 (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.

**Qwen3.5-122B-A10B.** A strong mid-size cousin for coding, agent steps and reading images or video, with thinking you can switch off per request. Alibaba's 4-bit build needs about 95 GB, which fits one 128 GB unified-memory machine, and several providers host it. The main risk is silence, with no safety results, agent guidance or answers from Alibaba.

## 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

### Qwen3.5-122B-A10B

1. Quickest start on a 128 GB machine is `ollama run qwen3.5:122b` (Q4_K_M, 81 GB of files, about 97 GB with context)
2. Alibaba's 4-bit build serves with `vllm serve Qwen/Qwen3.5-122B-A10B-GPTQ-Int4 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --quantization moe_wna16`, though that kernel isn't documented for AMD gfx1151
3. Use the qwen3_coder tool parser and the qwen3 reasoning parser, as the card's commands do
4. Turn thinking off with `"chat_template_kwargs": {"enable_thinking": false}` in the request body, since it's on by default and `/think` and `/nothink` aren't supported
5. Sampling per the card is temperature 0.6, top_p 0.95 and top_k 20 when thinking, and temperature 0.7, top_p 0.8, top_k 20 and presence_penalty 1.5 without
6. Keep the context at 128K or more if you rely on thinking, and turn on YaRN only past 262,144 tokens
7. Add `--language-model-only` in vLLM to skip the vision encoder and free memory for the KV cache when you only need text
8. Validate any JSON it returns, since structured output isn't documented

## Questions

### Which is better for AI agents, DeepSeek-V4-Flash-0731 or Qwen3.5-122B-A10B?

Qwen3.5-122B-A10B scores 63.6 (B) on agent readiness against DeepSeek-V4-Flash-0731's 62.4 (B), and leads in 2 of 5 scored categories. DeepSeek-V4-Flash-0731 leads on agent interface.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-122b-a10b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-qwen3-5-122b-a10b.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "deepseek-v4-flash-0731", "b": "qwen3-5-122b-a10b"}`. From a terminal: `anchor compare deepseek-v4-flash-0731 qwen3-5-122b-a10b`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepseek-v4-flash-0731.json and https://www.anchorterminal.com/api/v1/tools/qwen3-5-122b-a10b.json

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