# InternVL3.5-GPT-OSS-20B-A4B-Preview vs Mistral Large 4 > Mistral Large 4 scores 54.6 (C) to InternVL3.5-GPT-OSS-20B-A4B-Preview's 51.2 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side. - Canonical: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mistral-large-4 - Markdown: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mistral-large-4.md (~7,750 tokens) - Slim: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mistral-large-4.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mistral-large-4.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 InternVL3.5-GPT-OSS-20B-A4B-Preview's 51.2 (D), and leads in 3 of 4 scored categories. InternVL3.5-GPT-OSS-20B-A4B-Preview leads on reach. Both do open-weight models you run yourself. - InternVL3.5-GPT-OSS-20B-A4B-Preview: grade D, 51.2/100, rank #104 of 117 open-weight models. Markdown https://www.anchorterminal.com/tools/internvl3-5-gpt-oss-20b-a4b-preview.md · JSON https://www.anchorterminal.com/api/v1/tools/internvl3-5-gpt-oss-20b-a4b-preview.json - 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 - 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 ### InternVL3.5-GPT-OSS-20B-A4B-Preview (D) Good for: Agents with 64 GB to spare that want an Apache-licensed image and video reader with optional thinking and can live with a stale preview. Ahead on: - Reach, 60 against 50 Watch for: No quantised build from OpenGVLab, so about 51 GB at BF16 ### 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 55 - Agent interface, 95 against 45 - Documentation and upkeep, 64 against 28 Watch for: The smallest 4-bit build needs about 732 GB, so nothing fits a single workstation or a 128 GB machine ## Score by category | Category | Weight | InternVL3.5-GPT-OSS-20B-A4B-Preview | Mistral Large 4 | Edge | | --- | --- | --- | --- | --- | | Capability | 35%, pending | pending | pending | not scored in this run | | Reach | 25% (25 this run) | 60 | 50 | InternVL3.5-GPT-OSS-20B-A4B-Preview +10 | | Risk | 20% (20 this run) | 55 | 75 | Mistral Large 4 +20 | | Agent interface | 10% (10 this run) | 45 | 95 | Mistral Large 4 +50 | | Documentation and upkeep | 10% (10 this run) | 28 | 64 | Mistral Large 4 +36 | | Negative events | ≤15 | 0 | 0 | | | **Total** | | **51.2 · D** | **54.6 · C** | | ## Facts side by side | Fact | InternVL3.5-GPT-OSS-20B-A4B-Preview | Mistral Large 4 | | --- | --- | --- | | Kind | Open-weight model | Open-weight model | | Vendor | OpenGVLab (Shanghai AI Laboratory) | Mistral AI | | Hosted endpoint | no (local only) | no (local only) | | Transports | | | | Auth | None | None | | Pricing | Free | Free | | x402 | no | no | | Licence | Apache-2.0; a merge of openai/gpt-oss-20b (Apache 2.0 with OpenAI's usage policy) and OpenGVLab/InternViT-300M-448px-V2_5 (MIT) | Mistral Ecosystem Open-Weight License (modified MIT) | | Read-only variant documented | no | no | | llms.txt | no | no | | Last release | 2025-09-08 | 2026-10-29 | | 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 | 1.8/5 (4) | none | ## Verdicts **InternVL3.5-GPT-OSS-20B-A4B-Preview.** An image and video reader on gpt-oss-20b with an optional thinking mode, a preview untouched for over a year. It needs about 51 GB in BF16, since OpenGVLab publishes no quantised build, and runs in Transformers and vLLM. The main risk is a chat template in the Transformers-format repository that doesn't match its tokenizer, with no safety results. **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. ## Before you call either ### InternVL3.5-GPT-OSS-20B-A4B-Preview 1. Serve with `vllm serve OpenGVLab/InternVL3_5-GPT-OSS-20B-A4B-Preview-HF --max-model-len 32768`, which is our command; the card gives none for vLLM 2. Check the rendered prompt before relying on it, since the shipped template's <|im_start|> markers aren't tokens in the gpt-oss tokenizer; the GitHub-format repository's harmony template is the alternative 3. Use Transformers 4.55.0 or later, as the card requires for this size 4. For thinking, send the card's thinking system prompt and parse the reasoning out of the tags, with temperature 0.6 and sampling on as the card advises 5. Keep inputs within 32K tokens, the length it was trained at, whatever the config says ### 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 ## Questions ### Which is better for AI agents, InternVL3.5-GPT-OSS-20B-A4B-Preview or Mistral Large 4? Mistral Large 4 scores 54.6 (C) on agent readiness against InternVL3.5-GPT-OSS-20B-A4B-Preview's 51.2 (D), and leads in 3 of 4 scored categories. InternVL3.5-GPT-OSS-20B-A4B-Preview leads on reach. ## For agents - This comparison as JSON: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mistral-large-4.json, and with the fewest tokens: https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-mistral-large-4.min.md - Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "internvl3-5-gpt-oss-20b-a4b-preview", "b": "mistral-large-4"}`. 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[Mistral Large 4 vs SmolLM3-3B](https://www.anchorterminal.com/compare/mistral-large-4-vs-smollm3-3b.md) - [Mistral Large 4 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/mistral-large-4-vs-spark-x2-6-4b.md) - [Mistral Large 4 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-27b.md) - [Mistral Large 4 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/mistral-large-4-vs-swift-1-5-qwen3-8-flash-next.md) - [Mistral Large 4 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/mistral-large-4-vs-thinkingcap-qwen3-8-27b.md) - [Mistral Large 4 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/mistral-large-4-vs-unlimited-ocr-2.md) --- Query this catalogue directly over MCP: https://www.anchorterminal.com/mcp (Streamable HTTP, no key). How: https://www.anchorterminal.com/agents/#mcp