# Apertus v1.5 8B vs DeepSeek-V4-Flash-0731

> DeepSeek-V4-Flash-0731 scores 62.4 (B) to Apertus v1.5 8B'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/apertus-v1-5-8b-vs-deepseek-v4-flash-0731
- Markdown: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731.md (~6,650 tokens)
- Slim: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/apertus-v1-5-8b-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 Apertus v1.5 8B's 61 (C), and leads in 1 of 4 scored categories. Apertus v1.5 8B leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.

- Apertus v1.5 8B: grade C, 61/100, rank #35 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/apertus-v1-5-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.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

### Apertus v1.5 8B (C)

Good for: Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.

Ahead on:
- Risk, 75 against 50
- Agent interface, 85 against 75
- Documentation and upkeep, 84 against 43

Watch for: No released runtime supports it, only Swiss AI's forks of vLLM and Transformers

### 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 40

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 | Apertus v1.5 8B | DeepSeek-V4-Flash-0731 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 40 | 70 | DeepSeek-V4-Flash-0731 +30 |
| Risk | 20% (20 this run) | 75 | 50 | Apertus v1.5 8B +25 |
| Agent interface | 10% (10 this run) | 85 | 75 | Apertus v1.5 8B +10 |
| Documentation and upkeep | 10% (10 this run) | 84 | 43 | Apertus v1.5 8B +41 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **61 · C** | **62.4 · B** | |

## Facts side by side

| Fact | Apertus v1.5 8B | DeepSeek-V4-Flash-0731 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Swiss AI Initiative (EPFL and ETH Zurich) | DeepSeek |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate | MIT |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-14 | 2026-07-31 |
| Terms last updated |  | 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 | 2.8/5 (4) | 2.5/5 (4) |

## Verdicts

**Apertus v1.5 8B.** A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.

**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

### Apertus v1.5 8B

1. Accept the terms on the card and set HF_TOKEN before downloading
2. Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet
3. Serve with `--chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertus` for tools, and leave thinking off when you need tools
4. For thinking, set `--reasoning-parser apertus` and `--default-chat-template-kwargs.enable_thinking true`, and budget max tokens generously
5. Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames
6. Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32
7. If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent

### 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, Apertus v1.5 8B or DeepSeek-V4-Flash-0731?

DeepSeek-V4-Flash-0731 scores 62.4 (B) on agent readiness against Apertus v1.5 8B's 61 (C), and leads in 1 of 4 scored categories. Apertus v1.5 8B leads on risk, agent interface and documentation and upkeep.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731.json, and with the fewest tokens: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "apertus-v1-5-8b", "b": "deepseek-v4-flash-0731"}`. From a terminal: `anchor compare apertus-v1-5-8b deepseek-v4-flash-0731`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json and https://www.anchorterminal.com/api/v1/tools/deepseek-v4-flash-0731.json

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