Head to head · Open-weight models you run yourself · October 2026 research run
Mistral Large 4 vs OTel-2.0-LLM-31B-IT
OTel-2.0-LLM-31B-IT scores 56.8 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 1 of 4 scored categories. Mistral Large 4 leads on risk and agent interface. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
Which one, for what
Best 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 60
Watch for
The smallest 4-bit build needs about 732 GB, so nothing fits a single workstation or a 128 GB machine
Best for Telecoms teams that want an open model for standards questions and RAG on their own hardware
Ahead on
- Documentation and upkeep, 74 against 64
Watch for
BF16 only at 62.6 GB, with no GGUF or other quantised build from anyone we checked
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Agent interface Mistral Large 4 +35
Mistral Large 4 95
The chat template is in the repository (20). The card documents how tools are declared and come back and names the parser (25), and the template renders several calls in one turn (20). Strict JSON Schema output is documented with how to ask for it (15). Reasoning effort is budgeted per request with the reasoning returned apart from the answer (10). Context scores 5, because the 1M window comes from YaRN scaling and the technical report gives 40K as the longest pretraining sequence. 20 + 25 + 20 + 15 + 10 + 5 = 95.
OTel-2.0-LLM-31B-IT 60
Chat template 20, chat_template.jinja in the repository, 19,300 characters, the publisher's own. Tool-calling format 10, only the template says how tools are declared and how calls come back. Trained tool calling 20, the template loops over tool_calls, so it renders several calls in one turn, and the card says the training mix included general-purpose agentic tool-calling examples, while none were specific to telecoms. Structured output 0, the card doesn't document JSON or JSON Schema output. Reasoning control 0, the card says the model wasn't trained to produce or consume Gemma's thought channel and answers directly, so there is no thinking to switch or budget. Context 10, 262,144 positions in the text config, the base's native context. 20 + 10 + 20 + 0 + 0 + 10 = 60.
Risk Mistral Large 4 +20
Mistral Large 4 75
File safety scores 25, safetensors only and loaded by Transformers' own code with a Jinja template. Provenance scores 15, Mistral's verified organisation with a hash on every file. Safety evaluation scores 10, refusal and prompt-injection results Mistral ran itself, listed in its 2 November technical report as internal, and red-teaming by outside firms with nothing published. Agent guidance scores 10 for a statement of what the refusals cover and a claim of trained injection resistance, with only general safeguards. Licence risk scores 10, 5 off for the Model-as-a-Service limit. The security programme scores 5 for security.txt with no public advisories. 25 + 15 + 10 + 10 + 10 + 5 = 75.
OTel-2.0-LLM-31B-IT 55
File safety 25, safetensors only, with no .bin, .pt or .pth files and no Python files. Provenance 5, the files sit in a personal account, farbodtavakkoli, not in an organisation AT&T's or GSMA's site links to: GSMA's article names AT&T as the releaser without linking the repository, and the GitHub repository that links it is the same person's (0). Safety evaluation 0, no safety results on the card. Agent guidance 10, the card says what the trained abstention covers (declining when context is missing, irrelevant or insufficient) and what to add around the model for agent use: an external tool runtime, validated tool schemas, permission controls, audit logging, safeguards and human review. Licence risk 15, Apache 2.0 has no use policy, field-of-use limit, threshold or change clause, nothing is added on the card, and the licence link resolves (200 on 10 November 2026). Security programme 0, no way to report a flaw found. 25 + 5 + 0 + 10 + 15 + 0 = 55.
Documentation and upkeep OTel-2.0-LLM-31B-IT +10
Mistral Large 4 64
The card covers intended use, architecture, parameters, context and languages, and misses limitations and a cut-off (14). Training data is described by its mix in the technical report, with no sources, cut-off or filtering (5). The files changed on 2 November 2026, when the report was added (20). A Mistral member replied in three of nine discussions (10). It's a first release with older Large models in their own repositories and no dated note (5), and the files agree with each other and the card (10). 14 + 5 + 20 + 10 + 5 + 10 = 64.
OTel-2.0-LLM-31B-IT 74
Model card 14, Intended use, a long limitations section, the architecture and training method, the parameters (31B) and the language (English) are on the card. Training data 15, the card names the sources (3GPP, ETSI, ITU, GSMA, CAMARA, O-RAN and TM Forum material provided by GSMA), the overall sizes (15 billion raw tokens, over 1 trillion processed, about 440 billion used), the mix of example types and the processing tool, and lists what was left out. Last change 20, the last commit, on 1 November 2026, updated the card's links, resources, citation and contacts, nine days before this check. Responsiveness 20, Of the 3 discussions that aren't pull requests (7 August to 28 October 2026), 2 have a reply from the repository's owner, which is most. Version policy 0, the weights were replaced in place: uploads on 27 July, 3 August and 1 September 2026 under one name, with a standing notice on the card that they may change and that users should pin a revision, and no dated note of what changed. File health 5, the card describes 15 shards of about 62.6 GB while the repository still holds an earlier 14-shard set beside them, 126.8 GB in all. 14 + 15 + 20 + 20 + 0 + 5 = 74.
Score by category
| Category | Weight this run | Mistral Large 4 | OTel-2.0-LLM-31B-IT | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 50 | 50 | even |
| Risk | 20%20 | 75 | 55 | Mistral Large 4 +20 |
| Agent interface | 10%10 | 95 | 60 | Mistral Large 4 +35 |
| Documentation and upkeep | 10%10 | 64 | 74 | OTel-2.0-LLM-31B-IT +10 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 54.6/100 | C 56.8/100 |
Facts side by side
| Fact | Mistral Large 4 | OTel-2.0-LLM-31B-IT |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Mistral AI | AT&T (Open Telco AI) |
| 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) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-29 | 2026-11-01 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
Verdicts
Mistral Large 4
A 1T-parameter mixture-of-experts model from Mistral 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 no released vLLM or SGLang loads it. The licence bars Model-as-a-Service businesses without a separate deal.
OTel-2.0-LLM-31B-IT
A real telecoms post-training of Gemma 4 31B IT with an unusually candid card on what was and wasn't trained. It fits a 128 GB machine in BF16 only, with no quantised build, sits in a personal account, and its weights have been replaced in place. Tool-call format, structured output and safety results aren't documented.
Before you call either
Mistral Large 4
- 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 - On H200 use the FP8 build with tensor parallelism of 16 or 8 by 2 pipelines, per vLLM's recipe
- Pass
reasoning_effortlow, medium or high (default high); any other value makes the template raise an error, and there is no off switch - Read the reasoning from
message.reasoning;max_completion_tokenscounts reasoning tokens too - Use temperature 1.0 and top_p 0.95 at every effort, per the card
- Ask for JSON with
response_formattype json_schema and strict true; the template passes the schema to the model - The template rejects a tool message straight after a user message and a conversation that doesn't start with a user or system message
- Add
--speculative-config '{"method": "dspark", "num_speculative_tokens": 3}'to use the embedded draft head - 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
OTel-2.0-LLM-31B-IT
- Download the 15 shards the index names, not the whole repository, which is twice the size
- Pin a revision. The card says the weights may change under the same name
- Serve it with vLLM 0.19.0 or later and the repository's own chat template, not Gemma 4's
- Send no thinking flags. It answers directly and wasn't trained on Gemma's thought channel
- Use it as the generation stage of a RAG pipeline over telecom standards, with source attribution and human review
- Don't use it to run network tools unattended. The card says that hasn't been evaluated
Questions
Which is better for AI agents, Mistral Large 4 or OTel-2.0-LLM-31B-IT?
OTel-2.0-LLM-31B-IT scores 56.8 (C) on agent readiness against Mistral Large 4's 54.6 (C), and leads in 1 of 4 scored categories. Mistral Large 4 leads on risk and agent interface.
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- OTel-2.0-LLM-31B-IT vs WeVisOne-VL-4B
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Machine-readable
- This page as Markdown
/compare/mistral-large-4-vs-otel-2-0-llm-31b-it.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/mistral-large-4.json·/api/v1/tools/otel-2-0-llm-31b-it.json - From a terminal
anchor compare mistral-large-4 otel-2-0-llm-31b-it(the CLI) - Over MCP
compare_tools {"a": "mistral-large-4", "b": "otel-2-0-llm-31b-it"}at/mcp, no key