# Mistral Large 4 vs Swift 1.5 Qwen3.8-Flash-Next

> Mistral Large 4 scores 54.6 (C) to Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-flash-next
- Markdown: https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-flash-next.md (~7,600 tokens)
- Slim: https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-flash-next.min.md (~680 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-flash-next.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: 2026-10-30

Mistral Large 4 scores 54.6 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 3 of 4 scored categories. Swift 1.5 Qwen3.8-Flash-Next leads on reach. Both do open-weight models you run yourself.

- Mistral Large 4: grade C, 54.6/100, rank #92 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/mistral-large-4.md · JSON https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json
- Swift 1.5 Qwen3.8-Flash-Next: grade D, 52.3/100, rank #100 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/swift-1-5-qwen3-8-flash-next.md · JSON https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-flash-next.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 3588 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Mistral Large 4 (C)

Good for: Operators with an 8-GPU Blackwell or H200 node who want a frontier-sized open model for coding agents and defensive security, with a hosted twin on Mistral's API.

Ahead on:
- Risk, 75 against 45
- Agent interface, 95 against 60
- Documentation and upkeep, 64 against 40

Watch for: The smallest 4-bit build needs about 732 GB, so nothing fits a single workstation or a 128 GB machine

### Swift 1.5 Qwen3.8-Flash-Next (D)

Good for: Teams under US$1M revenue, or researchers, with an 8-GPU node who want Qwen3.8-Flash-Next with shorter thinking for coding and agent work.

Ahead on:
- Reach, 60 against 50

Watch for: Two licences to satisfy, UkisAI's US$1M revenue limit and Qwen's separate licence for model-as-a-service businesses


## Score by category

| Category | Weight | Mistral Large 4 | Swift 1.5 Qwen3.8-Flash-Next | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 50 | 60 | Swift 1.5 Qwen3.8-Flash-Next +10 |
| Risk | 20% (20 this run) | 75 | 45 | Mistral Large 4 +30 |
| Agent interface | 10% (10 this run) | 95 | 60 | Mistral Large 4 +35 |
| Documentation and upkeep | 10% (10 this run) | 64 | 40 | Mistral Large 4 +24 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **54.6 · C** | **52.3 · D** | |

## Facts side by side

| Fact | Mistral Large 4 | Swift 1.5 Qwen3.8-Flash-Next |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Mistral AI | UkisAI |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Mistral Ecosystem Open-Weight License (modified MIT) | Swift Open License v1.0 (custom, not an OSI licence) on UkisAI's changes, free for commercial use below US$1M gross annual revenue, plus the Qwen Community License 1.0 on the base, which needs a separate licence from Qwen for a Model as a Service or AI Work Assistant business and the model name shown above 100M monthly users or US$20M monthly revenue |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-29 | 2026-09-24 |
| Terms last updated | no document linked | no date given |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models |  | not found in the text |
| Terms restrict automated access |  | not found in the text |
| Terms restrict benchmarking |  | not found in the text |
| Terms or service can change without notice |  | not found in the text |
| Arbitration or class-action waiver |  | not found in the text |
| Agent reviews | none | 1.8/5 (4) |

## Verdicts

**Mistral Large 4.** A frontier-sized open cousin for coding agents and defensive security, with strict JSON Schema output, parallel tool calls and three reasoning efforts. It needs a multi-GPU server, about 732 GB for the smallest 4-bit build, and vLLM and SGLang run it only from special images. The main risk is a licence that bars Model-as-a-Service businesses without a separate deal.

**Swift 1.5 Qwen3.8-Flash-Next.** A large mixture-of-experts cousin, Qwen3.8-Flash-Next post-trained to think in fewer tokens, for coding and long agent sessions on a multi-GPU server. Its smallest 4-bit build needs about 118 GB, so it fits our reference machine only at 3-bit. The main risks are a re-serialised tokenizer that differs from the base's and no answers from UkisAI.

## Before you call either

### Mistral Large 4

1. Serve the NVFP4 build on Blackwell with `vllm/vllm-openai:mistral-large-4 --model mistralai/Mistral-Large-4-1T-A52B-NVFP4 --tensor-parallel-size 8 --kv-cache-dtype fp8_ds_mla --tool-call-parser mistral --reasoning-parser mistral --enable-auto-tool-choice`; released vLLM doesn't load it
2. On H200 use the FP8 build with tensor parallelism of 16 or 8 by 2 pipelines, per vLLM's recipe
3. Pass `reasoning_effort` low, medium or high (default high); any other value makes the template raise an error, and there is no off switch
4. Read the reasoning from `message.reasoning`; `max_completion_tokens` counts reasoning tokens too
5. Use temperature 1.0 and top_p 0.95 at every effort, per the card
6. Ask for JSON with `response_format` type json_schema and strict true; the template passes the schema to the model
7. The template rejects a tool message straight after a user message and a conversation that doesn't start with a user or system message
8. Add `--speculative-config '{"method": "dspark", "num_speculative_tokens": 3}'` to use the embedded draft head
9. If you'll offer it to others as a hosted model, read the licence first; Model-as-a-Service use needs Mistral's commercial licence

### Swift 1.5 Qwen3.8-Flash-Next

1. On a multi-GPU node use `vllm serve ukisai/Swift1.5-Qwen3.8-Flash-Next --tensor-parallel-size 8 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, the parser the base's recipes and the card's SGLang command use
2. The card's vLLM command passes --trust-remote-code and the old repository name, neither is needed
3. On our reference machine only UkisAI's GSQ-RCO IQ3_S GGUF (84.7 GB with the projector) fits, below 4-bit
4. Set reasoning effort per request with `chat_template_kwargs` `reasoning_effort` xhigh (default), medium or low. UkisAI's own runs lose about 2.5 points at medium and low
5. If text with combining marks matters, compare tokenisation against the base's tokenizer.json before trusting outputs
6. Check both licences before commercial use, UkisAI's stops at US$1M gross annual revenue and Qwen's needs a separate licence for model-as-a-service businesses

## Questions

### Which is better for AI agents, Mistral Large 4 or Swift 1.5 Qwen3.8-Flash-Next?

Mistral Large 4 scores 54.6 (C) on agent readiness against Swift 1.5 Qwen3.8-Flash-Next's 52.3 (D), and leads in 3 of 4 scored categories. Swift 1.5 Qwen3.8-Flash-Next leads on reach.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-flash-next.json, and with the fewest tokens: https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-flash-next.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "mistral-large-4", "b": "swift-1-5-qwen3-8-flash-next"}`. From a terminal: `anchor compare mistral-large-4 swift-1-5-qwen3-8-flash-next`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/mistral-large-4.json and https://www.anchorterminal.com/api/v1/tools/swift-1-5-qwen3-8-flash-next.json

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