The fastest broad-use models in this directory are Gemini 3.1 Flash and Claude 4 Haiku. If your workflow is mostly coding, Codestral 25.01 is the fastest specialist option.
The safest this comparison default, the cheaper option worth trying first, and the specialist pick — before you read the detail below.
Gemini 3.1 Flash is the strongest answer here for this comparison — pick it when quality of output matters more than the $0.50/1M/1M input you pay for it.
Claude Opus 4.5 is the cheaper way in for this comparison, at $5.00/1M/1M input against Gemini 3.1 Flash's $0.50/1M/1M.
Claude 4 Haiku 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.
Gemini 3.1 Flash is the fastest broad-use default here.
Claude 4 Haiku is the fastest writing-first option.
Codestral 25.01 is the fastest coding-focused option in the budget tier.
Fastest is only useful if quality stays high enough for the task.
For chat interfaces and high-volume prompts, broad-use speed usually matters more than peak reasoning depth.
For coding, specialist speed often matters more than all-around versatility.
Switch the scoring lens to see whether the this comparison answer changes when cost, speed, or long-document depth leads the decision.
Google / Budget / Sep 2, 2026
Best cheap AI for broad day-to-day work — now with 1M context.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You need premium reasoning depth or the highest coding benchmark scores.
Where the this comparison recommendation shifts once you weigh price or latency differently.
Best cheap AI for broad day-to-day work — now with 1M context.
Best low-cost writing option for fast-moving content teams.
Best budget-focused coding specialist for high-volume developer teams.
Solid OpenAI budget option, though Gemini Flash offers better value.
List prices and published scores — the numbers this page's pick is built from.
| Model | Input | Output | Est. month | Context | Speed | Coding | Writing | Research |
|---|---|---|---|---|---|---|---|---|
| Gemini 3.1 FlashGoogle | $0.50/1M | $3.00/1M | $11 | 1M tokens | Very fast | 68 | 75 | 76 |
| Claude 4 HaikuAnthropic | $0.80/1M | $4.00/1M | $16 | 200k tokens | Very fast | 52 | 85 | 62 |
| Codestral 25.01Mistral | $0.90/1M | $2.70/1M | $14 | 256k tokens | Very fast | 88 | 38 | 52 |
| GPT-5.2 MiniOpenAI | $1.20/1M | $4.80/1M | $22 | 128k tokens | Fast | 78 | 72 | 68 |
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.
The default answer for this comparison — 75/100 on the writing axis, and the model we would start with unless the price below rules it out.
Fast, low-cost model with a 1M token context window — the best budget default for teams running high prompt volumes.
You need premium reasoning depth or the highest coding benchmark scores.
The best all-around budget model for most teams. Faster than its predecessor, cheaper, and with a 1M context window that outclasses every other budget option.
Full pricing, benchmark table and release notes on the Gemini 3.1 Flash page.
Here for latency: it answers fastest of anything listed for this comparison, at 85/100 on writing.
Fast and affordable Anthropic option that keeps writing quality surprisingly high for the price.
Cost is your only concern — Gemini 3.1 Flash offers similar value with a larger context window.
The best pick when you want Anthropic quality at a budget price point — especially for writing-heavy automations.
Full pricing, benchmark table and release notes on the Claude 4 Haiku page.
Also worth a look for this comparison, at 38/100 on the writing axis.
Best budget-focused coding specialist for high-volume developer teams. Full Codestral 25.01 review →
Rounds out the shortlist for this comparison at 72/100 on writing.
Solid OpenAI budget option, though Gemini Flash offers better value. Full GPT-5.2 Mini review →
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
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Gemini 3.1 Flash is the fastest broad-use model in this directory for most teams, with Claude 4 Haiku close behind for text-first workflows.
Codestral 25.01 is the fastest coding-focused option in this directory while still staying genuinely useful for engineering work.
Claude 4 Haiku is the fastest writing-focused option in this directory for quick drafts, rewrites, and content operations.
Not always, but the fastest broad-use models in this directory also happen to be strong value picks, especially Gemini 3.1 Flash and Claude 4 Haiku.
Choose speed when the task is repetitive or low-risk. Choose quality when mistakes, rework, or missed edge cases are expensive.
Two different things, and they matter in different places. Time-to-first-token is how long you wait before anything appears — it dominates how responsive a chat interface feels. Throughput is how many tokens per second arrive after that, and it dominates how long a large generation takes end to end. A model can be good at one and mediocre at the other. Our speed ratings blend both, which is why a model rated 'Very fast' is a safe pick for an interactive product and a model rated 'Deliberate' is not.
They generate hidden intermediate tokens before answering. That thinking pass is real compute you wait for and, on most providers, real tokens you pay for. It buys genuine accuracy on maths, multi-step logic, and hard debugging — and it is wasted on summarising an email. This is the single biggest speed variable in the directory: the gap between a fast non-reasoning model and a deliberate reasoning one is far larger than the gap between two models in the same class.
In batch and background work, almost entirely. If a job runs on a schedule, nobody is watching the cursor blink, so a slower and more accurate model usually wins outright. Speed earns its keep in three places: interactive chat, autocomplete-style coding assistance, and high-volume pipelines where latency multiplied across millions of calls becomes throughput you have to provision for. Outside those, optimise for correctness and cost first.