Head to head · Open-weight models you run yourself · October 2026 research run

Mistral Large 4 vs ZAYA1-8B

ZAYA1-8B scores 61 (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, agent interface and documentation and upkeep. 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

Mistral Large 4 C

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
  • Documentation and upkeep, 64 against 50

Watch for

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

ZAYA1-8B C

Best for People who want a small, fast reasoning model for maths and code and can live without tool calling

Ahead on

  • Reach, 80 against 50

Watch for

Tool calling needs a parser that only Zyphra's vLLM fork has

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.

ZAYA1-8B 60

Chat template 20, chat_template.jinja in the repository (10,700 characters). Tool-calling format 10, only the template and a fork say it. Trained tool calling 10, the template takes tools and renders calls, and the card recommends settings for agent use, but it doesn't say the model was trained for tool calling or for several calls in a turn, so the lower band. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch, and the card names the qwen3 reasoning parser, which returns the reasoning apart from the answer. Context 10, 131,072 positions in config.json, for an architecture Zyphra trained end to end, so the config is the model's own. 20 + 10 + 10 + 0 + 10 + 10 = 60.

Reach ZAYA1-8B +30

Mistral Large 4 50

Memory scores 0, since the smallest 4-bit build, Mistral's NVFP4, is 610 GB, about 732 GB with a fifth added, far above 256 GB. Runtimes score 10, released Transformers and llama.cpp have the architecture, while vLLM v0.33.0 and SGLang v0.5.23 still lack it, the llama.cpp build Ollama 0.40.14 pins predates it, and LM Studio has no entry. Builds score 15 for Mistral's FP8 and NVFP4 releases. Terms score 10, a modified MIT licence that is free commercially except for Model-as-a-Service businesses. Download scores 10 with no gate, and hosted too scores 5 because Mistral's API serves these weights. 0 + 10 + 15 + 10 + 10 + 5 = 50.

ZAYA1-8B 80

Memory 30, the smallest build from Zyphra is the MXFP4-Experts repository, 5.85 GB, and 5.85 GB × 1.2 = 7.0 GB, within 16 GB. Runtimes 10, Released Transformers 5.22.2 has the zaya model and SGLang 0.5.23 has srt/models/zaya.py. Builds 15, Quantised builds from Zyphra at two precisions, FP8 and MXFP4 for the experts, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no API. Zyphra's site lists a cloud product for its open models, which wasn't checked for this model. 30 + 10 + 15 + 15 + 10 + 0 = 80.

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.

ZAYA1-8B 55

File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the Zyphra organisation, which the card ties to zyphra.com's blog post for the model (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 0, no way to report a flaw on the card, and www.zyphra.com has no security.txt. 25 + 15 + 0 + 0 + 15 + 0 = 55.

Documentation and upkeep Mistral Large 4 +14

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.

ZAYA1-8B 50

Model card 5, Intended use and the parameter counts are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the main repository last changed on 26 June 2026 (the checkpoint reshaped for Transformers), 137 days before this check, and the quantised repositories on 2 July, 131 days. Responsiveness 20, Of the 16 most recent discussions that aren't pull requests (7 May to 14 August 2026), 10 have a reply from a Zyphra member, which is most. Version policy 5, the original checkpoint is kept as ZAYA1-8B-legacy and the card says so at the top, without a date. File health 10, Config, template and card agree on the architecture after the reshape. 5 + 0 + 10 + 20 + 5 + 10 = 50.

Score by category

CategoryWeight this runMistral Large 4ZAYA1-8BEdge
Capability35%pendingpendingpendingnot scored in this run
Reach25%255080ZAYA1-8B +30
Risk20%207555Mistral Large 4 +20
Agent interface10%109560Mistral Large 4 +35
Documentation and upkeep10%106450Mistral Large 4 +14
Negative events≤1500
TotalC 54.6/100C 61/100

Facts side by side

FactMistral Large 4ZAYA1-8B
KindOpen-weight modelOpen-weight model
VendorMistral AIZyphra
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMistral Ecosystem Open-Weight License (modified MIT)Apache-2.0
Read-only variant documentednono
llms.txtnono
Last release2026-10-292026-07-02
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno 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.

ZAYA1-8B

A small reasoning model trained from scratch by one lab, with a clean licence, 4-bit and FP8 builds and a team that answers questions. It's the hardest of the small models to run: its own architecture, a card that points at forks, and tool calling that needs a parser no release has. Little on limits, data or safety.

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

ZAYA1-8B

  1. Use it for reasoning, maths and code. Don't count on tool calling outside Zyphra's vLLM fork
  2. Run it with Transformers 5.22.2 or SGLang 0.5.23, which have the model built in
  3. Use temperature 1.0 and top_p 0.95 in general, and temperature 0.6 for agent and code work
  4. Download Zyphra/ZAYA1-8B, not the legacy repository, for the released runtimes
  5. The quantised repositories have no licence tag. The main one is Apache 2.0

Questions

Which is better for AI agents, Mistral Large 4 or ZAYA1-8B?

ZAYA1-8B scores 61 (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, agent interface and documentation and upkeep.

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