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Choosing an open-weight AI model is easiest when you make three decisions in order: license, task, then hardware. For a single 24 GB-class computer, the practical starting points are Qwen3.8-27B or Gemma 4 for general writing, Qwen3-Coder 30B for local coding agents, and Z-Image-Turbo or FLUX.2 [klein] 4B for commercially usable image generation. Larger agent models such as DeepSeek V4.1 Flash belong on a server or hosted endpoint, while the current image-quality leaders Qwen-Image-2.1 and FLUX 3 Image Klein have licensing limits that can rule them out for business use.
“Open-weight” only means the parameter files can be downloaded. It does not promise an Open Source Initiative-approved license, affordable memory requirements, or good output at the maximum context length. The right choice is the model you can legally deploy, run at the precision you intend to use, and validate on your own work.
At a glance
| Name | Best for | Key spec | Platforms | Checked |
|---|---|---|---|---|
| Qwen3-Coder 30B | Local coding agents | 30.5B total, 3.3B active, 262,144-token context; Apache 2.0 | Ollama, llama.cpp, vLLM, MLX packages | November 2026 |
| Qwen3.8-27B | General writing, multilingual work and local multimodal chat | 27.78B; 262,144-token context; image input; Apache 2.0; about 16.5 GB at the default 4-bit build | Ollama, llama.cpp, LM Studio, vLLM | November 2026 |
| Gemma 4 26B/31B | Fast general assistant on a workstation | 25.2B or 31B; Apache 2.0; roughly 16–19 GB for the 26B version | Ollama, llama.cpp, LM Studio | November 2026 |
| DeepSeek V4.1 Flash | High-volume hosted agents and long-context work | 552B MoE, 8B active for prefill and 16B decode; 1M context; MIT; about 510 GB of files | vLLM and hosted APIs | November 2026 |
| Z-Image-Turbo | Commercial local image generation on modest GPUs | 6B; Apache 2.0; 8 steps; about 6.7–9.1 GB with offloading | ComfyUI | November 2026 |
| FLUX.2 [klein] 4B | Commercial image generation in an 8–12 GB setup | About 3.9–4B; Apache 2.0; ungated; about 6.8 GB at 8-bit with offloading | ComfyUI | November 2026 |
| Qwen-Image-2.1 | Maximum open-weight image quality when research-only licensing is acceptable | 7B DiT; open-weights Elo 1045 on 7 November 2026; Qwen Research License | ComfyUI | November 2026 |
| FLUX 3 Image Klein | High-quality images for companies below the license revenue threshold | 5.6B plus Qwen3-VL-4B encoder; Elo 1043; commercial outputs below US$5 million annual revenue under its community license | ComfyUI | November 2026 |
What does open-weight mean?
An open-weight model publishes the trained parameters for download. You can usually copy those files to your own machine, but the permission to use them is defined by the accompanying license and policy, not by the word “open.” The distinction is explained in the Licenseware license guide and the Guide to Artificial Intelligence comparison.
Licenses fall into four practical groups:
- Permissive software licenses: Apache 2.0 and MIT generally allow commercial use with notices and attribution requirements. Current examples include Gemma 4, gpt-oss, Qwen3.8-27B, DeepSeek V4.1 Flash, Mistral Small 4 and Muse Glimmer 30B.
- Community licenses: commercial use may be allowed until a user, revenue or product threshold is reached. Llama 4, Kimi K3.1 Pro, Muse Spark Open and some Qwen checkpoints have additional naming, attribution or service terms.
- Use-restricted licenses: prohibited-use policies or geographic exclusions can change whether a product is allowed, even when the weights are downloadable.
- Research or non-commercial licenses: useful for evaluation, but unsuitable for a client deliverable without a separate agreement. Qwen-Image-2.1 is a current image-generation example.
OLMo is unusual because it publishes weights, training code, the Dolma corpus and training logs, and is described as meeting the Open Source AI Definition. Most popular families are open-weight rather than fully open-source in that sense.
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How should you choose an open-weight model?
Use this sequence for every candidate. It prevents an impressive benchmark from sending you toward a model you cannot ship or cannot fit in memory.
- Check the exact license and jurisdiction. Read the model card’s license field, the repository LICENSE file and any usage policy. Check user, monthly-revenue and annual-revenue thresholds, attribution, naming, derivative-model and distillation clauses. Save a dated copy of the accepted license. The 13k.eu license survey is a useful map, not legal advice.
- Match the task family. A text model that understands photographs is not automatically an image generator. Separate writing and retrieval, repository coding, tool-using agents, image understanding and diffusion image creation.
- Size the model for the precision you will deploy. A model card’s BF16 size is not the same as a Q4_K_M file. Include context cache, runtime overhead and image text-encoder memory.
- Test at your real context length. A 262K or 1M-token ceiling does not guarantee reliable reasoning at that length. Long prompts also consume memory and reduce throughput.
- Check the ecosystem. Look for current Ollama tags, llama.cpp or vLLM support, ComfyUI workflows, fine-tunes, LoRAs, ControlNet support and a release cadence.
- Run a private evaluation. Fifty representative examples, scored identically for every candidate, are more useful than combining unrelated public leaderboards. Pin the checkpoint, tokenizer, runtime and quantization before comparing results.
Which license is safe for a commercial deployment?
Apache 2.0 or MIT is usually the least complicated starting point, but you still need to preserve notices and comply with applicable law. Custom licenses can permit commercial use while adding obligations that matter to a hosted service or embedded product.
| License situation | Examples | What to verify before shipping |
|---|---|---|
| Apache 2.0 or MIT | Gemma 4, gpt-oss-20b/120b, Qwen3.8-27B, Mistral Small 4, DeepSeek V4.1 Flash and V4 Pro, Muse Glimmer 30B | Keep notices, follow the usage policy and confirm the checkpoint has the stated license. |
| Community license with thresholds | Kimi K3.1 Pro, Muse Spark Open, Qwen3.8-Flash-Next and Qwen3.8 Max | Monthly active users, annual or monthly revenue, attribution, display names and separate Model-as-a-Service terms. |
| Llama 4 Community License | Llama 4 Scout | “Built with Llama” and derivative naming rules; a 700-million-monthly-active-user deal; multimodal rights are not granted to individuals domiciled in the EU or companies headquartered there. |
| Research or non-commercial | Qwen-Image-2.1, Fibo 2.0, some FLUX and other image checkpoints | Do not use for client or product work unless the rightsholder grants permission. |
For an EU-facing multimodal product, do not assume a general Llama permission covers the image-capable checkpoint. For image models, inspect whether the license covers the model itself, generated outputs, commercial revenue and redistribution. A license that permits outputs may still forbid shipping the model inside your product.
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Which open-weight model is best for writing and general chat?
For a local general assistant, start with Qwen3.8-27B when you want multilingual breadth and native image input, Gemma 4 26B when speed matters, Mistral Small 4 when concise answers and European provenance matter, or gpt-oss-20b when 16 GB is the hard memory limit. There is no defensible single winner across all writing workloads.
| Model | Why choose it | Memory and limits | License |
|---|---|---|---|
| Qwen3.8-27B | Broad multilingual coverage, including Chinese and Indian languages; vision input; long native context | 27.78B dense; 262,144-token context; LU Labs reports a 16.5 GB default 4-bit build | Apache 2.0 |
| Gemma 4 26B/31B | Good speed-to-quality range, from phone-class E2B/E4B to workstation models; image support | 26B version is about 16–19 GB; the 31B model needs more headroom | Apache 2.0 |
| Mistral Small 4 | Brevity and efficiency; a practical choice for summaries and structured responses | Use the published quantized file size rather than the family name to plan memory | Apache 2.0 |
| gpt-oss-20b | Reasoning and tool use when the machine has about 16 GB available | 21B total with 3.6B active; the vendor card says it runs within 16 GB using MXFP4 | Apache 2.0 plus a usage policy |
| Muse Glimmer 30B | A larger local option for multimodal workstation use | About 16.8 GB file size; aim for a 24 GB card after context overhead | Apache 2.0 |
The older size rule remains useful: in the 7B–9B range, Qwen or Gemma usually offer the best quality per gigabyte; around 24B–32B, Gemma, Qwen and Mistral are close; at 70B and above, larger Llama, Qwen and DeepSeek families become more capable but require server-class memory. Translate those bands to the current names rather than downloading an older checkpoint simply because a guide still lists it.
Writing workloads that need a hosted model
For extraction, summarization, retrieval-augmented generation and high-volume chat, DeepSeek V4.1 Flash is a cost-oriented option. CheapestInference lists MIT licensing, image input, a 1M-token context, an Artificial Analysis v4.3.2 index of 45 and list pricing of $0.30 per million input tokens and $1.20 per million output tokens, checked November 2026. Those are published figures, not a guarantee for your prompts or provider.
Which open-weight model should I use for coding locally?
Qwen3-Coder 30B is the most practical first candidate for a workstation coding agent. Its 3.3B active parameters keep token generation manageable while the 262,144-token context leaves room for repository files, command output and recovery instructions. For a smaller machine, use a 7B–14B coding checkpoint and accept weaker repository-level reasoning; for serious agent runs, move to a server model rather than forcing a 30B or larger file into swap.
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| Deployment | Models to evaluate | Why | Important caveat |
|---|---|---|---|
| One 24 GB workstation | Qwen3-Coder 30B; Qwen3.8-27B; Muse Glimmer 30B | Local context, tool use and multimodal repository workflows; the first two are Apache 2.0 at the cited checkpoints | Leave memory for KV cache and command output; use the exact quantization you plan to deploy. |
| Single 80 GB GPU | gpt-oss-120b | 117B total and 5.1B active; about 65 GB in Ollama; designed for one 80 GB GPU | Throughput depends on context, concurrency and runtime. |
| Server or hosted API | DeepSeek V4.1 Flash, GLM-5.3, Kimi K3.1 Pro and Qwen3.8 Max | Long context and stronger tool-use or coding capability | Files range from hundreds of gigabytes to several terabytes; do not plan a consumer-GPU download. |
For larger agent evaluations, compare completed-task rate, recovery after failed commands, latency and serving cost. Wavect recommends testing DeepSeek V4.1 Flash, GLM-5.3 and Kimi K3.1 Pro on the same tasks rather than copying a ranking from mixed vendor harnesses.
What do the published coding scores actually say?
CheapestInference reports Artificial Analysis v4.3.2 figures checked in November 2026: Kimi K3.1 Pro scored 55 on the AA Index, Qwen3.8 Max 45, GLM-5.3 45, DeepSeek V4.1 Flash 45 and GLM-5.3-Flash 42. The same report lists Terminal-Bench 2.1 scores of 90.6 for DeepSeek V4.1 Flash, 88.2 for GLM-5.3 and 86.6 for Qwen3.8 Max. Kimi’s cited vendor results include Terminal-Bench 4.0 at 65.7 and DeepSWE v1.1 at 75.5.
Do not mix those numbers with older AA editions: version 4.3.2 rescored models 12–19 points lower than version 4.1.1. Harnesses, reasoning settings and test sets differ, so use the figures to choose candidates, not to predict your repository’s success.
| Model | Published list price | Published evidence |
|---|---|---|
| Kimi K3.1 Pro | $2 input / $10 output per million tokens | AA Index 55; vendor DeepSWE v1.1 75.5 |
| Qwen3.8 Max | $2 / $6 per million tokens | SWE-bench Pro 67.7; Terminal-Bench 2.1 86.6 |
| GLM-5.3 | $1.40 / $4.40 per million tokens | Terminal-Bench 2.1 88.2 |
| DeepSeek V4.1 Flash | $0.30 / $1.20 per million tokens | Terminal-Bench 2.1 90.6; vendor DeepSWE v1.1 74.2 |
| GLM-5.3-Flash | $0.15 / $0.50 per million tokens | AA Index 42 |
Prices and figures in this table were reported or checked in November 2026. Provider billing, discounts and availability can change.
Which open-weight image model should I use?
Image generation is a separate stack from vision-language chat. A model such as Qwen3.8-27B can describe an image, but a diffusion or DiT checkpoint in ComfyUI creates the pixels. The text encoder can consume more memory than the image transformer, so size the complete workflow rather than only the headline parameter count.
When image quality matters most
On the Artificial Analysis AA-Image-T2I v2.1 open-weights board read on 7 November 2026, Qwen-Image-2.1 led with Elo 1045, FLUX 3 Image Klein followed at 1043 and Fibo 2.0 at 1039. Quantslant’s 29 October snapshot put FLUX at 1043 and Qwen at 1042. The one- or two-point gap is smaller than the reported margin of variation, so treat Qwen and FLUX as one quality tier rather than declaring a permanent winner.
| Model | Strength | License and business warning | Hardware note |
|---|---|---|---|
| Qwen-Image-2.1 | Top open-weight text-to-image quality; strong editing and on-image text | Qwen Research License; research and evaluation only unless separately authorized | Its encoder is reported at 17.5 GB versus 14.2 GB for the image model, so offload it. |
| FLUX 3 Image Klein | Near-tied open-weight quality and strong editing | Kommunity License; commercial outputs for companies under US$5 million annual revenue; shipping it in a product needs a commercial tier | Quantslant reports about 7.3–7.5 GB INT8 peak on an RTX 4090. |
| Fibo 2.0 | Strong open editing; Quantslant reports Elo 1088 for editing | CC BY-NC 4.0 and gated; not suitable for commercial client work | Plan for its 8B model and encoder requirements from the current card. |
These models remain behind closed image systems on aggregate quality. gpuwerk placed Qwen-Image-2.1 about 214 Elo behind the leading closed model, and an Arena snapshot on 6 November 2026 recorded 7,016,263 votes across 84 models. That gap matters if your priority is maximum fidelity, but it does not override a license requirement or a need for local data handling.
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Which image models can I use commercially without a revenue cap?
For a permissive local workflow, start with Z-Image-Turbo, FLUX.2 [klein] 4B, MOSS-Image, older Qwen-Image 1.0 or FLUX.1 [schnell]. Read the exact repository license before downloading a variant.
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|---|---|---|---|
| Z-Image-Turbo | Apache 2.0 | 6B; about 9.1 GB at 8-bit with encoder offloaded and 6.7 GB at the lowest reported setup; eight steps; CFG-free and does not use negative prompts | Commercial local generation on 8–12 GB cards |
| FLUX.2 [klein] 4B | Apache 2.0; ungated | About 3.9–4B; roughly 6.8 GB at 8-bit with offloading | Fast, permissive generation on a small GPU |
| MOSS-Image | Apache 2.0 | 5.1B; about 8.1 GB at 8-bit with offloading | Commercial experimentation in the 8–12 GB class |
| Qwen-Image 1.0 / 2512 | Apache 2.0 | 20B; about 13.1 GB at Q4_K_M and 23.7 GB at 8-bit in published estimates | On-image text and editing when 24 GB is available |
| FLUX.1 [schnell] | Apache 2.0 | 11.9B; older fast FLUX checkpoint | Established ComfyUI workflows and speed |
| HiDream-O1-Image | MIT | 8B; gpuwerk’s cited Arena score was 1115 | Permissive alternative worth private evaluation |
| Ming-Image-0.1-Design | MIT | 6B; Quantslant ranked it the highest fully permissive model on its open board at Elo 999 | Commercial design experiments where license simplicity matters |
SDXL and Illustrious are no longer quality leaders, but they remain useful for ControlNet, IP-Adapter and inpainting workflows. Illustrious has more than 196,000 LoRAs reported on Civitai, which can outweigh a lower base-model score for a specialized style.
How much VRAM do image models need?
- 8 GB: FLUX.2 [klein] 4B, SDXL or SD 3.5 Medium; Z-Image-Turbo and MOSS-Image at 4-bit. For commercial use, the Apache-licensed Klein 4B or Z-Image-Turbo are the straightforward choices.
- 12 GB: Z-Image-Turbo, MOSS-Image and FLUX 3 Image Klein at 8-bit.
- 16 GB: add FLUX.1 at about 14.9 GB in 8-bit. Some community-licensed models sit just above this once the encoder is included.
- 24 GB: larger workflows become practical; Qwen-Image 1.0 at 8-bit is reported at 23.7 GB, while FLUX.2 [dev] needs roughly 22.5 GB at 4-bit.
System RAM of at least 32 GB is sensible because image pipelines pair a DiT with a large language-model encoder. ComfyUI can encode the prompt and then offload the encoder, reducing peak VRAM. Published speed figures are hardware-specific: Black Forest Labs reported about four seconds for a 1-megapixel image on an RTX 5090 and under nine seconds on an RTX 4070 in an INT8 ComfyUI setup.
How much memory does a local LLM need?
A quick estimate is parameters × bits ÷ 8, followed by extra room for the context cache, runtime and operating system. An 8B model needs about 16 GB at 16-bit, 8 GB at 8-bit and 4–5 GB at 4-bit. A 70B model needs roughly 40 GB at 4-bit before overhead.
| Available memory | Practical starting range | Examples |
|---|---|---|
| 8 GB VRAM | About 3B–8B at Q4 | Gemma 4 E2B/E4B and smaller coding models |
| 12–16 GB | About 8B–14B; selected 20B-class files with aggressive quantization | gpt-oss-20b at the lower edge only if the published runtime recipe fits |
| 24 GB | About 14B–32B at 4-bit, depending on context | Qwen3.8-27B, Qwen3-Coder 30B, Gemma 4 26B and Muse Glimmer 30B |
| One 80 GB GPU | Roughly 70B–120B quantized, depending on cache and concurrency | gpt-oss-120b is sized for one 80 GB GPU |
| Multiple GPUs or hosted service | Models above roughly 300B total parameters | DeepSeek V4.1 Flash, GLM-5.3, Kimi K3.1 Pro and Qwen3.8 Max |
Mixture-of-experts models make the arithmetic less intuitive. Total parameters determine download and resident memory, while active parameters largely determine token speed and cost. A 235B model with 22B active parameters can generate more like a 22B dense model but still requires memory closer to its full weight set.
The practical rule is to choose a file at least 25 percent smaller than your available graphics or unified memory, then benchmark one weekly task at the intended context length. A 24 GB RTX 4090-class card appears repeatedly in both text and image guidance; on that Ada GPU, FP8 or GGUF is more practical than Blackwell-only NVFP4.
Should I use GGUF, AWQ, GPTQ or FP8?
Use the format that matches your runtime and deployment target:
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- GGUF: the llama.cpp container used by Ollama and many desktop tools. Q4_K_M is the usual quality-size balance; Q5_K_M and Q6_K retain more quality; Q8_0 is close to full precision.
- AWQ or GPTQ: GPU-oriented 4-bit formats commonly served with vLLM.
- FP8: a data-center-friendly format on recent hardware when throughput matters more than squeezing a model into a consumer card.
Four-bit quantization usually causes a small quality loss in ordinary chat and a larger one in exact code, mathematics and constrained output. Compare the same checkpoint at the precision you will actually deploy; comparing a BF16 server model with a Q4 desktop file is not an apples-to-apples evaluation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which runtime should I use?
For a desktop text model, Ollama is the simplest starting point and pulls Q4_K_M by default. LM Studio offers a graphical model manager. llama.cpp gives lower-level control over offload and context settings. vLLM is the normal choice for a server or API, including gpt-oss-120b and the large MoE models. Apple Silicon users can look for MLX packages for Qwen3.8-27B and Muse models.
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How do I run a fair private evaluation?
Build a small test set before you choose a favorite. Include about 50 real examples from the work you care about: representative writing prompts, multilingual passages, repository issues, tool calls, image prompts, edits and failure-recovery cases.
- Freeze the candidate checkpoint, tokenizer, runtime version and quantization.
- Use the same system prompt, tools, context window and sampling settings for every text model.
- Score correctness, instruction following, citation or extraction accuracy, latency, peak memory and cost.
- For coding agents, score completed tasks, safe command behavior, recovery after a failed test and unnecessary context usage.
- For images, keep seeds and output dimensions where appropriate, and score typography, anatomy, prompt adherence, edit fidelity and workflow reliability.
- Repeat difficult examples after a model update. A new checkpoint can change behavior even when the family name stays the same.
Keep the model card and your evaluation separate: the card defines what the model claims and permits; your private results decide whether it is useful for your workload.
What are the most common selection mistakes?
- Downloading before reading the license: a model can be technically local but legally unusable for your product or geography.
- Planning from parameter count alone: MoE total size, KV cache and image encoders can dominate memory.
- Trusting the maximum context: quality and speed often degrade before the advertised ceiling.
- Mixing benchmark editions: Artificial Analysis v4.3.2 is not directly comparable with v4.1.1.
- Confusing image understanding with image creation: use a diffusion or DiT checkpoint in ComfyUI for generation.
- Assuming popularity grants permission: download counts measure interest, not commercial rights.
- Failing to pin revisions: save the exact weight revision, tokenizer, runtime, quantization and license copy used in production.
What is the practical decision for each common setup?
8 GB laptop or desktop: choose Gemma 4 E2B/E4B for text, and FLUX.2 [klein] 4B or a quantized Z-Image-Turbo workflow for commercial images.
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16 GB memory: try gpt-oss-20b only with the published MXFP4 recipe and modest context; for images, Z-Image-Turbo, MOSS-Image or FLUX.1 [schnell] are more comfortable.
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24 GB workstation: begin with Qwen3.8-27B for general work, Qwen3-Coder 30B for coding agents and Z-Image-Turbo or FLUX.2 [klein] 4B for permissive image generation. Compare Gemma 4 26B when response speed is more important than depth.
80 GB GPU: evaluate gpt-oss-120b and server-oriented runtimes, but reserve enough memory for long contexts and concurrent requests.
Cluster or hosted API: test DeepSeek V4.1 Flash first for cost-sensitive agents, then GLM-5.3, Kimi K3.1 Pro and Qwen3.8 Max against your completed-task and recovery metrics. Do not buy hardware for a 500 GB-plus weight set unless you have a clear serving plan.
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Can a 24 GB graphics card run a 70B open-weight model?
Usually not at a useful context length. The 4-bit weights alone are roughly 40 GB, before the KV cache and runtime overhead; use a 14B–32B model locally or move the 70B model to multiple GPUs.
Is an open-weight model automatically open source?
No. Downloadable weights are the only guarantee. A fully open project also publishes relevant code, data or training details and uses an acceptable open license; OLMo is an uncommon example described as meeting the Open Source AI Definition.
Which license should an EU company prefer for a multimodal product?
Start by checking an Apache 2.0 or MIT checkpoint such as Qwen3.8-27B or Gemma 4, then read its current usage policy. Llama 4’s multimodal rights contain a specific restriction for EU-domiciled individuals and EU-headquartered companies, so do not rely on a generic “Llama is open” assumption.
Does 8-bit always look better than 4-bit?
Not for every task. Eight-bit generally preserves more exact code and mathematical behavior, while 4-bit saves substantial memory and is often adequate for chat; the only reliable answer is an evaluation at your real prompts and context length.
How can I keep a model deployment reproducible?
Record the exact checkpoint revision, tokenizer, quantization, runtime version, system prompt and license copy. Pin those artifacts in deployment, because a new file under the same family name can change output quality or legal terms.
