# Llama, Mistral, DeepSeek and Qwen: An Open-Weight Comparison

> Compare version-specific licenses and deployment requirements, then evaluate open-weight models with a reproducible quality, latency and cost protocol.

- Published: 2026-03-13
- Updated: 2026-09-08
- Author: Gene Dai
- Canonical: [https://digidai.github.io/2026/03/13/open-weight-ai-war-llama-mistral-deepseek-qwen/](https://digidai.github.io/2026/03/13/open-weight-ai-war-llama-mistral-deepseek-qwen/)
- Topics: AI, Open Weight Models, Llama, Mistral, DeepSeek, Qwen

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Llama, Mistral, DeepSeek and Qwen are model families, not four fixed products. A useful comparison starts with an exact repository and revision, then asks whether its license, serving requirements and observed behavior fit the intended application.

This page provides a selection method and four concrete release examples. It is not a leaderboard of the latest models or a report of new hands-on benchmark results. Source pages were checked on September 8, 2026; the examples remain explicitly versioned rather than being relabeled as current flagship recommendations.

The distinction is important. A model family can contain releases with different architectures, licenses, context handling and tool-use behavior. A claim about one release should not silently apply to every model carrying the same brand.

## Four repositories, four bounded comparisons

The following table records what the named publishers' model pages say. It does not establish legal approval for a particular use. Read the license text and relevant policies for the exact artifact you intend to deploy.

| Named release | Publisher-listed license | Practical question before evaluation |
| --- | --- | --- |
| [Llama 4 Scout 17B-16E Instruct](https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct) | Llama 4 Community License | Do the custom use, redistribution and other conditions fit the proposed deployment? |
| [Mistral Small 3.1 24B Instruct 2503](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503) | Apache 2.0 | Which serving and quantization configuration will be tested? |
| [DeepSeek V3 0324](https://huggingface.co/deepseek-ai/DeepSeek-V3-0324) | MIT | Can the available infrastructure operate this particular model under the required load? |
| [Qwen3 235B-A22B](https://huggingface.co/Qwen/Qwen3-235B-A22B) | Apache 2.0 | Are the runtime, template and execution settings correct for this release? |

These examples show why the license must be attached to the artifact, not the company name. They also show why "open-weight" is more precise than assuming every release supplies the same training code, data information or redistribution rights.

A permissive weight license does not automatically settle questions about input data, generated output, bundled software or a hosted provider's contract. A custom license is not automatically unusable. Both require a review tied to the actual deployment.

Do not replace a repository's license with the license on a convenient third-party wrapper. A wrapper, a quantized checkpoint and the original weights can be different artifacts with different provenance. Keep their identities in the deployment record.

## Downloading a model does not specify the system

An application typically needs more than weights. It needs a tokenizer and prompt format, a serving runtime, resource allocation, request handling, monitoring and an interface to the user's work. An agent also needs tools, permissions and a way to recover from failed actions.

That surrounding system can change the result substantially. A model that produces a plausible answer in a notebook may fail when the application requires a strict schema, a particular language or reliable behavior under concurrent load.

Treat the deployment as a versioned combination. Record the repository revision, checkpoint format, quantization, tokenizer, runtime version, prompt template, generation settings and hardware. Without that record, a later engineer may be unable to reproduce the result even with the same model name.

A hosted endpoint is a separate configuration again. Its provider may use a quantized model, different serving software, request limits or a moving model alias. If the provider does not disclose enough to reproduce the setup, label the result as an endpoint evaluation rather than a measurement of all deployments of the model.

This is not an argument against hosted services. It is a way to keep the claim at the level the evidence supports.

## Active parameters do not describe the whole memory requirement

Mixture-of-experts model names often advertise an active-parameter count. That describes only one aspect of execution. It does not, on its own, specify the total weights that must be stored, the memory layout, cache requirements or the serving arrangement.

The Llama and Qwen examples in the table make the naming issue visible. Do not compare the active number in one model's name with the total parameter count in another and treat that as a complete hardware comparison.

Memory also depends on numerical format, context length, batch size and runtime behavior. A configuration that can load the weights may still fail under the required context or concurrency. Loading successfully is an initial check, not production acceptance.

For a local evaluation, specify the hardware first and measure on that hardware. If offloading is involved, record it. If a third-party quantization is used, record its origin and exact file. A result that fits on a device is not necessarily a result that meets a user's latency requirement.

Mistral's named model card includes a local-deployment claim conditional on quantization. Preserve that condition when discussing it. Do not turn a vendor's statement about fitting on hardware into an independent test of speed, quality or sustained load.

## Context length and language support need task-level checks

A long advertised context window does not establish that a model will reliably use every relevant fact in a long document. A supported language does not establish accuracy in a specific domain, dialect or writing task.

Build evaluation examples from the application's real requirements. For long documents, place necessary information at different positions and include plausible distractions. Check whether the response cites the right evidence and whether the cited passage actually supports the answer.

For multilingual use, have qualified reviewers judge the output in the relevant languages. Translation fluency, factual accuracy and domain terminology are separate criteria. An English-only test cannot establish performance for a multilingual deployment.

Do not score an unsupported answer as correct merely because it is well written. Include examples where the system should ask for clarification, acknowledge missing evidence or refuse an unauthorized action. Those cases matter in enterprise applications that operate on incomplete records.

These are proposed evaluation methods. This page does not claim they have been run on the four listed releases.

## Compare cost per accepted result

A token price or a hardware rental price is only one part of operating cost. Include utilization, retries, failed requests, data movement, engineering maintenance and human review. Keep one-time setup costs separate from repeat inference costs.

Choose a useful unit. For a document workflow, it might be an accepted extraction with required fields and source references. For coding, it might be a reviewed change that passes the agreed tests. For customer support, it might be a resolved issue under a defined quality policy.

Then use the same unit across configurations. A faster model that requires more correction can cost more per accepted result. A model with a larger hardware requirement may still be economical at sufficient utilization. Neither conclusion follows from the parameter count alone.

The [Cursor and Copilot comparison](/2026/02/08/cursor-vs-github-copilot-ai-coding-tools-deep-comparison/) uses the same principle for commercial coding tools: account for the whole path to accepted work, including review and failed attempts.

Keep the economic claim separate from workforce claims. An improvement in a task metric does not establish what happens to hiring, wages or employment. The [AI and employment evidence register](/research/ai-employment/) explains those distinctions through specific studies.

## A model-selection record other people can reproduce

A useful evaluation record can fit in a structured table or a small repository. It should contain enough information for another team member to repeat the test and understand the decision.

Record the use case and its limits first. Which inputs are allowed? Which output must be produced? Which actions are forbidden? Who is the reviewer? Which errors are unacceptable even if the average score is high?

Preserve the test cases and expected outcomes before comparing outputs. Separate development examples used to tune prompts from held-out evaluation examples. If you improve a prompt after seeing a failure, record the change and rerun all configurations affected by it.

Report quality, latency and cost together. Include failures and uncertainty rather than just the best run. For variable generation, use repeated trials where appropriate and state how many were performed.

Keep a rejected configuration in the record with its reason: it failed to load, exceeded the memory budget, returned invalid output or missed a quality threshold. Those are example failure categories, not measured results for these releases. Without rejected configurations, a later reader cannot tell whether the winner was broadly suitable or simply the only configuration that ran.

Self-hosting also has an opportunity cost. If a team cannot maintain the serving stack, a hosted product with an acceptable contract may be the better operating choice. Conversely, a hosted endpoint that violates a data-location or permission requirement cannot be made suitable by a higher benchmark score. State which constraint drove the decision.

Finally, write a decision with a scope: this configuration met these criteria on these cases and this hardware. Avoid turning that result into a permanent ranking of model families.

## Agent friendliness is an application property too

A model can produce tool-call-shaped text while the surrounding application mishandles the action. Validate the request schema, authorization, confirmation requirements and result handling independently of the model's language quality.

Keep untrusted documents separate from system instructions. A retrieved page can contain useful evidence and malicious directions in the same text. The application should not grant the document authority to change the user's task or access additional systems.

Give agents stable resource identifiers, explicit schemas and meaningful error responses. When a task fails, return the failure rather than a generic success message. When data is missing, say what is missing.

Those are design requirements for the application, not special powers granted by a particular model license. The [research registers](/research/) on this site follow the same information principle: stable record links, plain-text formats and limitations that stay attached to the finding.

## The selection that survives a production review

Start with the exact model release and allowed use. Confirm a supported configuration. Evaluate the real task and its failure cases. Compare cost per accepted result. Keep a reproducible record.

A model may fail the evaluation for reasons unrelated to its general intelligence: an unsupported deployment constraint, an unsuitable license condition, unacceptable latency or a failure to meet the application's evidence requirements. That is still a useful finding.

Choose the configuration that meets the application's acceptance criteria and that the team can maintain. Keep the model ID, test cases and operating limits with the decision.

### Sources and maintenance

The model table links directly to publisher-owned repositories. Release examples are intentionally fixed; future revisions must change the model ID and supporting evidence together. No benchmark score or production cost is presented as independently measured here.

September 8, 2026 revision: replaced broad family-level rankings and licensing generalizations with four named-release examples. Removed unsupported universal superiority claims and added a reproducible deployment-evaluation method. The examples are not labeled as the latest releases.

[Send a correction](/contact/) with the exact repository, revision and affected claim.

## Continue reading

- [Nvidia and the $12.93 Billion Promise to Keep Hugging Face Open](https://digidai.github.io/2026/09/04/nvidia-hugging-face-open-platform-promise/)
- [Arthur Mensch: Mistral AI](https://digidai.github.io/2025/11/11/arthur-mensch-mistral-ai-ceo-deep-analysis/)
- [Timothée Lacroix: Mistral AI Co-Founder](https://digidai.github.io/2025/11/11/timothee-lacroix-mistral-cofounder-deep-analysis/)
- [Guillaume Lample: Mistral AI Co-Founder](https://digidai.github.io/2025/11/11/guillaume-lample-mistral-ai-cofounder-analysis/)