Compare open AI models side by side.

Specs decide more than vibes. This page puts every model in the catalog in one table, then links to the head-to-head comparisons people actually search for — the pairs that force a real trade-off between price, context, vision and capability.

Two rules keep the comparisons honest. First, the per-million-token prices are upstream API rates, not what you pay to chat here: UsingOpen is free within the daily limit, so price is a proxy for what a model costs to run, not a bill. Second, a larger model is not automatically better. Sparse models activate a fraction of their weights per token, and a smaller model tuned for your task often wins.

Head-to-head comparisons

• DeepSeek V4 Flash vs Qwen3.8 Flash — The cheapest serious coder against Alibaba's fast multimodal all-rounder.
• GLM 5.3 Flash vs DeepSeek V4 Flash — The site's default all-rounder against the cheapest heavy hitter.
• Qwen3.8 27B vs Gemma 4 31B — A careful Qwen against Google's budget writer and image reader.
• GPT-OSS 120B vs DeepSeek V4 Flash — OpenAI's open-weight reasoner against DeepSeek's long-context bargain.
• Kimi K3 vs DeepSeek V4 Pro — Two flagships: Moonshot's multimodal giant against DeepSeek's full-strength V4.
• GLM 5.3 vs GLM 5.3 Flash — Same family, opposite trade-offs: the full model is bigger; the Flash is multimodal.
• Nemotron 3.5 Lightning vs Ling 3.0 Flash VL — NVIDIA's fast text specialist against inclusionAI's cheap multimodal tool user.
• Qwen3.8 Flash vs Gemma 4 26B — Two fast multimodal models, one with a million-token window and one with zero-retention terms.
• MiniMax M3 vs Qwen3.8 Flash — MiniMax's video-and-agent model against Qwen's faster, cheaper all-rounder.
• DeepSeek V4.1 Flash vs DeepSeek V4 Flash — DeepSeek's new multimodal Flash against the older text-only bargain.
• GLM 5.2 vs GLM 5.3 — Two generations of Z.ai's long-work model, with a price gap and a capacity catch.
• Qwen3.8 2.4T vs Kimi K3 — The two trillion-parameter flagships: Qwen's text heavyweight against Kimi's multimodal giant.
• Nemotron 3 Ultra vs GPT-OSS 120B — A 550B orchestrator against OpenAI's small, fast open reasoner.
• Inkling vs Inkling Small — Thinking Machines' two open models: full-size depth against a faster, cheaper sibling.
• MiMo V2.6 Flash vs Ling 3.0 Flash VL — Two cheap multimodal models, one built for long media inputs and one for retention-friendly speed.
• MiMo V2.6 Pro vs Qwen3.8 Flash — Xiaomi's flagship against Alibaba's cheaper fast multimodal model.
• Muse Glimmer 30B vs Gemma 4 31B — Meta's compact agent model against Google's cheaper writer and image reader.
• DeepSeek V4 Pro 0423 vs Qwen3.8 2.4T — An earlier DeepSeek flagship against Alibaba's trillion-parameter reasoner.

Every model at a glance

Availability changes with upstream capacity; the picker on the chat page always shows what is ready right now. A model marked "Adding capacity" is paused temporarily rather than removed.

How to compare two models

1. Context window first. If the input is a whole repository or a long archive, models under a million tokens may not fit it at all.
2. Then modalities. Screenshots, charts and video rule out text-only models immediately.
3. Then active parameters and price. These predict how fast and expensive the model is to run — useful when a workflow repeats thousands of times.
4. Then retention and tools. ZDR matters for sensitive content; tool support decides whether an agent loop is possible.
5. Finally, test it. Send the same real prompt to both models in Compare mode and read the answers. Specs shortlist; output decides.

Frequently asked questions

Which open AI model is the best?

There is no single best model. A 30B text model can be the right choice for high-volume drafting, while a trillion-parameter flagship earns its price on hard coding or reasoning. Start with the job, then compare context window, modalities, tools, retention terms and price for the two or three models that fit.

How do I compare two models on UsingOpen?

Use Compare mode on the chat page: pick two models, send one prompt, and both answers stream side by side. The comparison pages here cover the specs and trade-offs first, so you can shortlist before you spend a prompt.

Does a bigger model always win?

No. Total parameters are a rough capacity signal, and mixture-of-experts models activate only a fraction per token. A smaller model tuned for your task can beat a larger generalist, cost less and answer faster. Benchmark on your own prompts before paying for size.

What does the price per million tokens mean?

It is the upstream API rate for processing a million tokens — roughly 750,000 words — and it is the fairest way to compare running costs across models. UsingOpen itself is free within the daily prompt limit, so you are not charged these rates here.

Which models can read images?

The multimodal models in the catalog are GLM 5.3 Flash, Qwen3.8 Flash, Qwen3.8 27B, Gemma 4 31B, Gemma 4 26B, DeepSeek V4.1 Flash, Inkling, Kimi K3, MiMo V2.6 Flash, MiMo V2.6 Pro, Ling 3.0 Flash VL, MiniMax M3, Inkling Small. If a task depends on screenshots, diagrams or video, start there; the text-only models cannot accept a picture at all.

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