12 models in this directory cost 50¢ or less per million input tokens. DeepSeek V4-Pro is the most capable of them ($0.435/1M), Mistral Small 3.1 is the absolute cheapest at $0.1/1M, and GPT-5.6 Luna gives you the largest context window (1.05M tokens) at this price level.
The safest this comparison default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
DeepSeek V4-Pro is the strongest answer here for this comparison — pick it when quality of output matters more than the $0.43/1M/1M input you pay for it.
DeepSeek V4-Flash handles the same job for about 68% less per token. Start here and only move up if the output is not good enough.
Qwen 3.8 Flash is the fastest of these for this comparison — worth it when latency is what the reader notices, not the last few points of reasoning depth.
DeepSeek V4-Pro is the most capable model under 50¢/1M — $0.435/1M input, $0.87/1M output, 1M context.
Mistral Small 3.1 is the absolute cheapest at $0.1/1M input — 100x cheaper than Claude Fable 5.1.
DeepSeek V4-Pro is the strongest budget coding pick (coding score 93/100).
Choose DeepSeek V4-Pro as your budget default — the best capability-per-dollar in this price band.
Choose Mistral Small 3.1 for very high-volume tasks like classification, tagging, and extraction.
Route hard tasks to a premium model and keep everything else here — a two-tier setup usually cuts spend 60–80%.
Switch the scoring lens to see whether the this comparison answer changes when cost, speed, or long-document depth leads the decision.
DeepSeek / Budget / Aug 6, 2026
Best open-weights flagship — near-frontier coding at a tenth of the price.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You need vision input, verified agentic performance, or predictable pricing (surge pricing and an announced increase loom).
Where the this comparison recommendation shifts once you weigh price or latency differently.
Best open-weights flagship — near-frontier coding at a tenth of the price.
Best budget model from a frontier lab — near-frontier scores at commodity price.
GPT-4o-class coding quality at under $0.30/1M — the best value in the directory.
Best agentic capability per dollar in the directory.
SWE-bench Pro 62.5 at sixteen cents per million input.
Native vision and video, MIT weights, fifteen cents per million.
Fastest budget multimodal model — 350 tokens/sec at Lite pricing.
Apache 2.0 agent model that runs on a 24GB GPU.
List prices and published scores — the numbers this page's pick is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| DeepSeek V4-ProDeepSeek | $0.43/1M | $0.87/1M | $6.09 | 1M tokens | Balanced | 93 | 80 | 85 |
| GPT-5.6 LunaOpenAI | $0.20/1M | $1.20/1M | $4.40 | 1.1M tokens | Fast | 88 | 85 | 84 |
| DeepSeek V3DeepSeek | $0.27/1M | $1.10/1M | $4.90 | 128k tokens | Fast | 87 | 74 | 80 |
| DeepSeek V4-FlashDeepSeek | $0.14/1M | $0.28/1M | $1.96 | 1M tokens | Fast | 87 | 74 | 78 |
Scores out of 100 — how we evaluate models. “Est. month” is 10M in / 2M out at list price: a ceiling, no discounts.
Why each one is on the shortlist for this comparison, what it is genuinely good at, and where we would steer you away from it.
Our pick for this comparison. It scores 93/100 on the coding axis we weight this page by, and nothing else in this shortlist matches it on output quality.
DeepSeek's 1.6T-parameter (49B active) MoE flagship with hybrid sparse attention — near-frontier coding and reasoning at roughly a tenth of closed-rival pricing, MIT-licensed open weights.
You need vision input, verified agentic performance, or predictable pricing (surge pricing and an announced increase loom).
The open-weights frontier flagship of 2026. Self-reported numbers flatter it and independent agentic scores land lower, but even discounted it's the most capability per dollar in the directory's upper tier — with MIT-licensed weights.
Full pricing, benchmark table and release notes on the DeepSeek V4-Pro page.
Also worth a look for this comparison, at 88/100 on the coding axis.
The small, fast, cheap tier of the GPT-5.6 family — near-frontier scores on many benchmarks at commodity pricing after its ~80% July price cut.
Your workload actually uses the long context window — recall drops to 41% past 512K tokens.
The budget disruptor of 2026. After the price cut, Luna delivers benchmark scores that embarrass models 10x its price. Just don't trust it with genuinely long context — recall collapses past 512K tokens.
Full pricing, benchmark table and release notes on the GPT-5.6 Luna page.
Rounds out the shortlist for this comparison at 87/100 on coding.
GPT-4o-class coding quality at under $0.30/1M — the best value in the directory. Full DeepSeek V3 review →
Where most budgets should land for this comparison — about 68% less per token than DeepSeek V4-Pro, and still 87/100 on the coding axis.
Best agentic capability per dollar in the directory. Full DeepSeek V4-Flash review →
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
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DeepSeek V4-Pro — $0.435/1M input tokens with the highest capability average in this price band. Best open-weights flagship — near-frontier coding at a tenth of the price.
Mistral Small 3.1 at $0.1/1M input and $0.3/1M output. It handles ultra-high-volume classification, summarisation, and lightweight vision tasks well despite the price.
DeepSeek V4-Pro, with a coding score of 93/100 at $0.435/1M input.
Budget models trail flagships on hard reasoning, nuanced writing, and complex multi-step coding. Claude Fable 5.1, the current capability leader, scores 99/100 on average vs 86/100 for DeepSeek V4-Pro — use cheap models for volume, not for your hardest work.
Yes — usually 3–5x more. DeepSeek V4-Pro charges $0.435/1M input but $0.87/1M output, so long responses drive the real bill. Our API cost calculator models both sides.