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Short answer: choose Llama 4 Scout when native multimodality and extremely long documents matter most, Mistral Small 4 when you want one Apache 2.0 model for reasoning, images, coding and agentic workflows, and Gemma 3 or 4 when a smaller deployment footprint or Google tooling is the priority. The license, exact checkpoint, quantization and workload still matter more than the family name.
There is no universal winner. A model that leads a vendor’s benchmark table may lose on your prompts, language mix, context length, latency target or available memory. Treat the specifications below as a starting point, then test the exact checkpoint you intend to deploy.
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What you are actually choosing
Llama, Mistral and Gemma are families, not single interchangeable models. Each family contains different parameter sizes, context limits, input types, training recipes and license terms. “Open-weight” means the model weights can be obtained and run or adapted under published conditions; it does not automatically mean unrestricted commercial use, open training data or identical rights across checkpoints.
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- Capability: reasoning, coding, image understanding, tool use and long-context retrieval.
- Operations: memory consumption, latency, throughput, quantization and serving-runtime support.
- Legal fit: the exact license and any obligations attached to the checkpoint.
- Workload fit: quality on your own documents, languages, prompts and failure tolerance.
Start with the use case, not the brand. A 270-million-parameter model can be the right answer for an embedded feature, while a very large model is wasteful; conversely, a long legal document may justify a model whose context window is the deciding specification.
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At-a-glance comparison
| Candidate | License identified by the official source | Published context | Input and capability notes | Status or deployment note |
|---|---|---|---|---|
| Llama 4 Scout | Llama 4 Community License Agreement | Up to 10 million tokens (Meta product page) | Natively multimodal; Meta describes text and multimodal experiences | Best starting point when very long context or native vision dominates |
| Mistral Small 4 | Apache 2.0 | Not stated in the March 2026 announcement | Text and image inputs; reasoning, coding and agentic capabilities | Current replacement for Small 3.1 in new integrations |
| Mistral Small 3.1 | Apache 2.0 | 128K tokens | Image understanding and text generation | Retired on 2025-11-30; documentation directs new users to Small 4 |
| Gemma 3 270M and 1B | Not stated in the supplied Gemma 3 model-card details | 32K tokens | Lightweight variants; verify the selected checkpoint’s input features | Useful where memory and latency dominate |
| Gemma 3 4B, 12B and 27B | Not stated in the supplied Gemma 3 model-card details | 128K tokens | Larger Gemma 3 variants; verify image and tool support for the exact checkpoint | Balances local size with a longer working context |
| Gemma 4 variants | Apache 2.0 | 128K to 256K tokens, depending on variant | Google ecosystem and multimodal options; capabilities vary by size | Consider when Apache licensing and Google tooling are important |
The table compares published specifications, not a quality leaderboard. “Up to 10 million” is a vendor-advertised maximum for Scout; it does not mean every prompt will run at that length with acceptable speed or memory use.
Licensing: the difference that can stop a deployment
Llama 4 is distributed under Meta’s Llama 4 Community License Agreement rather than Apache 2.0. That custom agreement is the reason a technically excellent model may still be the wrong choice for a product. Read the license attached to the exact checkpoint, check its usage conditions and keep a copy with your release records. Do not assume that rights applying to an earlier Llama generation automatically apply to Llama 4.
Mistral identifies Small 4 as Apache 2.0. Its Small 3.1 announcement also identified that model as Apache 2.0, but Small 3.1 is retired, so a new project should review Small 4’s current files and notices instead of building around the older release.
Google’s Gemma 4 card identifies Apache 2.0. The supplied Gemma 3 card details do not establish the same term, so verify the Gemma 3 checkpoint’s current license before commercial distribution. A family-level assumption is not a substitute for the file that governs your model.
Commercial-use checklist
- Record the exact repository name, revision or checksum and quantized file you will ship.
- Open the license included with that checkpoint, not only a product summary.
- Check attribution, notice, acceptable-use and redistribution requirements.
- Confirm that adapters, fine-tuned weights, tokenizer files and vision components have compatible terms.
- Ask counsel to review the complete distribution path if the model is embedded in a paid product or exposed as an API.
Apache 2.0 is generally familiar to engineering teams, but it still requires compliance with its notice and license conditions. A custom community license may impose additional restrictions or definitions that your standard open-source checklist does not capture.
Context length and multimodality
When a long context window matters
Context is the maximum amount of text or multimodal content the model can consider in one interaction. It is not the same as the amount it can remember across sessions, and it is not a promise of perfect recall at the limit. Retrieval quality, attention dilution, prompt structure and runtime memory all affect the result.
Llama 4 Scout has the largest published ceiling in this comparison: Meta advertises up to 10 million tokens. That makes it the natural candidate for very large document sets, code repositories or long-running analysis, provided your serving stack and budget can sustain the workload.
Gemma 3 lists 32K for the 270M and 1B variants and 128K for the 4B, 12B and 27B variants. Gemma 4’s listed variants span 128K to 256K. Mistral Small 3.1 offered 128K, while current Mistral documentation marks it retired and points users to Small 4; the Small 4 announcement does not state a context number in the supplied material.
Vision and other inputs
Llama 4 is natively multimodal. Mistral Small 4 accepts text and image inputs and combines them with reasoning, coding and agentic capabilities. Small 3.1 provided image understanding. Gemma’s multimodal behavior depends on the specific generation and variant, so confirm that the checkpoint you download accepts the image formats and resolution your application sends.
Tool or function calling is a separate integration question. A model may be able to reason about a tool description without producing the exact schema your runtime expects. Test argument formatting, refusal behavior, retries and parallel calls with your own tool definitions.
Model profiles
Llama 4 Scout: the long-context and native-vision option
Scout is the clearest first evaluation when your input is natively multimodal or exceptionally long. Meta’s model card describes Llama 4 as natively multimodal, and its product page advertises the 10-million-token maximum for Scout. That combination can simplify applications that would otherwise split documents, summarize images separately or maintain a large retrieval pipeline.
The trade-off is governance and operations. You must accept the Llama 4 Community License Agreement, then validate memory, latency and throughput at the context lengths you actually plan to serve. A theoretical maximum is not a sensible production default: most requests should be measured at realistic lengths, with limits that protect queue time and cost.
Mistral Small 4: the current Apache-licensed generalist
Mistral announced Small 4 in March 2026 as an Apache 2.0 model combining reasoning, multimodal input, coding and agentic workflows. It is the strongest fit when one model must cover several of those jobs and your organization prefers Apache licensing.
The announcement does not provide a context figure in the supplied specifications, so obtain the current model card before promising a window to users. Also verify the image processor, tokenizer, quantized releases and serving instructions for the precise Small 4 checkpoint. Those details can change memory and latency even when the family name stays the same.
Mistral Small 3.1: useful evidence, not a new default
Small 3.1 remains useful as a concrete deployment reference. Mistral said its 24-billion-parameter-class model could run on one RTX 4090 graphics card or on a Mac with 32GB of RAM, and it offered a 128K context window. Those figures describe that release and its tested configuration; they are not a guarantee that Small 4, another quantization or a different runtime will fit identically.
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Current Mistral documentation marks Small 3.1 retired on November 30, 2025 and says to use Small 4 for new integrations. Keep 3.1 only when you have a compatibility reason, a validated existing deployment or a reproducibility requirement.
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Gemma 3: choose the smallest variant that clears your quality bar
Gemma 3 offers a broad size range. The 270M and 1B variants have 32K context, while 4B, 12B and 27B variants list 128K. That spread lets you trade quality and capability against memory, power and response time without changing families.
For local experimentation, begin with the smallest variant that passes a representative test set. Move up only when failures are attributable to model capacity rather than prompting, retrieval or tool wiring. Confirm image support, tokenizer behavior and license terms for the exact Gemma 3 checkpoint instead of inferring them from another size.
Gemma 4: Apache licensing with a 128K–256K range
Gemma 4’s official card identifies Apache 2.0 and lists contexts from 128K to 256K across its variants. It is attractive when Google’s ecosystem, an Apache license and a moderate-to-long context are more valuable than Scout’s advertised extreme ceiling.
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What hardware do you need for local use?
There is no single GPU requirement for any of these families. Memory depends on parameter count, numerical precision, quantization, context length, batch size, image processing and runtime overhead. The same weights can fit at one-bit-width and fail at another once the key-value cache grows during a long request.
Use the published 4090 reference correctly
Mistral’s Small 3.1 announcement says the 24B-class model can run on a single RTX 4090 graphics card or a Mac with 32GB of RAM. Treat this as a model-specific reference point for that release, not as a blanket answer for Llama 4, Small 4 or Gemma. Before buying hardware, reproduce the intended quantization and context length in the runtime you will use.
A practical sizing procedure
- Fix the checkpoint. Write down the exact model variant, revision and whether it includes vision components.
- Select a precision. Compare the publisher’s available quantized files with the full-precision option. Lower precision reduces memory but can change accuracy, formatting and tool reliability.
- Estimate peak memory. Include model weights, runtime overhead, tokenizer, vision encoder, operating-system use and the key-value cache for your maximum prompt and output.
- Load a safety margin. Leave headroom for concurrent requests, longer-than-average prompts and allocator fragmentation.
- Measure the real workload. Record prompt length, time to first token, sustained tokens per second, peak memory and error rate.
For a small local assistant, a Gemma 3 270M, 1B or 4B checkpoint may be easier to operate than a larger family member. For image-heavy or long-document work, the accelerator must be evaluated with both the language weights and multimodal pipeline loaded. A model that technically starts may still be too slow for interactive use.
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Vendor benchmark tables are directional. Mistral explicitly notes that scores can vary from previously reported results because evaluations use different internal pipelines. Prompt templates, few-shot examples, decoding settings, tool wrappers, test-set versions and scoring scripts can all change the result.
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For a fair comparison, record all of the following:
- benchmark name and version;
- exact checkpoint and quantization;
- prompt format and system message;
- zero-shot or number of shots;
- decoding settings and maximum output;
- metric and scoring script;
- hardware, runtime and date.
Then build a small private test set from the tasks that matter: document extraction, code repair, image questions, tool calls, refusal cases and the languages your users actually write. Run identical prompts through the same harness, inspect failures manually and measure latency as well as accuracy. No independent head-to-head result in the supplied material justifies naming one family the universal winner.
Decision guide by use case
Very long documents or native vision
Start with Llama 4 Scout. Its advertised 10-million-token ceiling and native multimodality address the defining requirement. Confirm that your infrastructure can serve the desired context and that the Llama 4 Community License Agreement is acceptable.
One Apache-licensed model for several jobs
Evaluate Mistral Small 4. Its announced combination of reasoning, images, coding and agentic workflows reduces the need to maintain separate models. Verify its current context specification and runtime support before committing.
Lightweight local deployment
Evaluate Gemma 3 at 270M, 1B or 4B first, depending on the quality bar and context requirement. Move to 12B or 27B when tests show that the smaller variants cannot handle your reasoning, coding or extraction tasks.
Google-centered tooling or Apache licensing
Gemma 4 is a logical candidate because its official card identifies Apache 2.0 and lists 128K–256K contexts. Compare the exact variant with Small 4 on your prompts rather than assuming ecosystem fit guarantees better output.
Existing Small 3.1 deployment
Keep Small 3.1 only when migration risk, compatibility or reproducibility outweighs the benefits of moving. For a new integration, follow current documentation and evaluate Small 4 instead; the older model is marked retired.
Fine-tuning, quantization and serving checks
Fine-tuning support is checkpoint-specific. Before training, confirm that the base weights, tokenizer, image processor and adapter method are all compatible and that the license permits your intended distribution. Save the training configuration and base revision so you can reproduce results.
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Quantization is an engineering trade-off, not a free speed setting. Test factual accuracy, code execution, structured-output validity, image descriptions and tool-call syntax at each precision you may deploy. A lower-memory file that increases retries or human review may cost more overall.
Serving compatibility deserves its own test. Check whether your runtime supports the model architecture, rotary or attention settings, multimodal inputs, streaming, batching and the tool schema you use. Validate cancellation, timeouts and out-of-memory recovery under concurrent load, not only in a one-request notebook.
Common mistakes and fixes
Mistake: treating a family as one model
Fix: record the exact variant, revision, parameter size, context setting and quantization in every experiment and deployment document.
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Fix: review the checkpoint license. Llama 4 uses Meta’s custom community agreement; Gemma 4 and Mistral Small 4 are identified as Apache 2.0, while Gemma 3 terms must be checked for the selected release.
Mistake: sizing hardware from weight memory alone
Fix: include key-value cache, multimodal components, runtime overhead, concurrency and the longest supported prompt. Benchmark peak memory with production-like requests.
Mistake: comparing unaligned vendor scores
Fix: use one harness, one prompt format and one metric, and publish the checkpoint and date alongside every score.
Mistake: building a new integration on a retired checkpoint
Fix: check current documentation first. Mistral’s documentation says Small 3.1 is retired and directs new integrations to Small 4.
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A step-by-step selection workflow
- Define the dominant requirement. Choose among long context, image input, coding, agents, low memory, Apache licensing or an existing runtime constraint.
- Shortlist one family and one neighboring alternative. For example, Scout plus Gemma 4 for long documents, or Small 4 plus Gemma 4 for an Apache-licensed generalist comparison.
- Check legal terms before engineering. Remove any checkpoint whose license cannot support your distribution model.
- Choose two or three exact variants. Include a smaller, faster option and a larger quality option when memory allows.
- Prepare a representative evaluation set. Include normal requests, edge cases, images, long inputs and malformed tool calls.
- Measure quality and operations together. Capture answer quality, structured-output validity, latency, throughput, peak memory and failure recovery.
- Stress the intended maximum. Test long contexts and concurrent requests at the limits you will advertise, not only short prompts.
- Freeze the release. Record checkpoint revision, quantization, runtime version, license text and evaluation date.
Bottom line
Choose Llama 4 Scout for the strongest long-context and native-multimodal starting point when Meta’s community license and infrastructure requirements fit. Choose Mistral Small 4 when an Apache 2.0 generalist for reasoning, vision, coding and agents is the priority. Choose Gemma 3 or 4 when you need a lightweight variant, Google ecosystem integration or Apache 2.0 terms. Whichever family you select, make the final decision with the exact checkpoint and quantization running on your own workload.
Quick Recap
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