LLM·Dex
Newsletter
Use caseTop 5 picks

Fastest LLMs in 2026

Highest output-tokens-per-second for latency-sensitive workloads.

Updated

How we ranked

  • Output tokens per second (sustained)
  • Time-to-first-token
  • Latency consistency under load
  • Quality at speed (no garbage tokens to fill time)
  • Hosting options that prioritize speed (Groq, Cerebras)

Read the full methodology for our sourcing and ranking standards.

Speed has become a primary axis of model differentiation. For interactive UX, the difference between 50 tokens/sec and 200 tokens/sec is the difference between "a tool I tolerate" and "a tool I love."

Gemini Flash, Haiku 4, and the GPT-5 small models all clear 100 tokens/sec on first-party APIs. For absolute peak speed, Groq and Cerebras host open-weight models at 500-1000 tokens/sec, a regime where the model finishes faster than you can read.

Speed comes with tradeoffs in quality. A reasoning model at 200 tokens/sec sounds dreamy, but reasoning is slow by design. Pick the speed tier that matches the task.

The ranking

  1. #1Google

    Gemini 3 Flash

    Google's high-speed, low-cost mid-tier with the same massive context window, popular for high-volume RAG.

    Full specsTry Gemini 3 Flash
    Context
    1.0M tokens
    Output · 1M
    Pricing not published
    Modalities
    text, vision, audio, video

    Why it ranks here. 1M-token context at mid-tier price. Very fast, good for interactive UX. Tracked weakness: Reasoning quality below Pro.

  2. #2OpenAI

    GPT-5 nano

    OpenAI's smallest GPT-5 variant, built for ultra-low-cost classification, routing, and high-volume inference.

    Full specsTry GPT-5 nano
    Context
    400K tokens
    Output · 1M
    $0.40 / 1M tokens
    Modalities
    text, vision

    Why it ranks here. Lowest-cost OpenAI model with vision support. Fast P99 latency. Tracked weakness: Visible quality gap on open-ended tasks.

  3. #3Anthropic

    Claude Haiku 4

    Anthropic's smallest 4-tier model, fast and cheap with the family's signature tone.

    Full specsTry Claude Haiku 4
    Context
    200K tokens
    Output · 1M
    Pricing not published
    Modalities
    text, vision

    Why it ranks here. Fast and cheap for an Anthropic model. Inherits Claude's sensible defaults. Tracked weakness: Quality gap visible on creative tasks.

  4. #4OpenAI

    GPT-5 mini

    GPT-5's mid-tier sibling, most of the quality at a fraction of the price, ideal for high-volume production workloads.

    Full specsTry GPT-5 mini
    Context
    400K tokens
    Output · 1M
    $2.00 / 1M tokens
    Modalities
    text, vision, audio

    Why it ranks here. Excellent price-quality ratio for production workloads. Fast first-token latency. Tracked weakness: Quality gap vs. flagship visible on hard reasoning.

  5. #5MetaOpen weights

    Llama 4 8B

    Meta's small Llama 4, built for on-device and edge inference.

    Full specs
    Context
    128K tokens
    Output · 1M
    Pricing not published
    Modalities
    text

    Why it ranks here. Runs on consumer laptops. Broad tooling support. Tracked weakness: Quality limited by size.

How to choose

Don't pick on the headline ranking alone. Run your top two picks on a representative sample of your own workload and compare. The numbers in this list are sound, but task-specific quality varies in ways no benchmark fully captures. The criteria above are the right axes to evaluate on, but the weighting depends on your stack.

  • Cost-sensitive workloads, start with the cheapest of the top three; escalate only if quality is the bottleneck.
  • Privacy-sensitive workloads, filter to open-weight picks above. They're labeled with a green badge.
  • Latency-sensitive workloads, see the Fastest LLMs list, which can override task-specific picks.

Frequently asked

  • What is the best model for fastest llms?
    Our #1 pick is Gemini 3 Flash from Google. Google's high-speed, low-cost mid-tier with the same massive context window, popular for high-volume RAG.
  • How are these rankings determined?
    We rank by the criteria listed at the top of this page: Output tokens per second (sustained); Time-to-first-token; Latency consistency under load. Where two models are close, we prefer the one with stronger production deployment evidence at the time of writing. Read the full methodology for our standards.
  • Gemini 3 Flash or GPT-5 nano?
    Both are top-tier picks. Gemini 3 Flash edges ahead on the criteria most relevant to this task. GPT-5 nano is the strongest alternative, see the head-to-head comparison page for full deltas.
  • Are open-source models on this list?
    Yes where they're competitive. Each entry below shows whether the model ships open weights and under what license.
  • How often is this list updated?
    Weekly. New launches that affect the ranking get reflected within seven days. The "last updated" stamp at the top of the page reflects the most recent dataset commit.

Related guides

Friday digest

The week's AI launches, in your inbox.

One short email every Friday, new models, leaks, and quietly-shipped APIs you missed.