How close does Workers AI get to the frontier? An October 2026 scorecard

For most of its life, Workers AI was where you went for embeddings, Whisper, and an 8B Llama for classification. Serious reasoning work went to a closed API. That changed in 2026. Since March, Cloudflare has been adding trillion-parameter open-weight models with million-token context windows, and it has been rebuilding its inference stack to serve them.

So I wanted a direct answer to one question. If you build on Workers AI today, how far behind the frontier are you, and what does it cost you?

The short version:

• Workers AI lists 69 models. 35 of them are text-generation models.
• The best hosted LLM, GLM-5.3, scores 45 on the Artificial Analysis Intelligence Index. The leader, Claude Opus 5.5, scores 58. That puts the best Workers AI model at about 78% of the top score.
• GLM-5.3-Flash scores 42 and costs about 4% of what Opus 5.5 costs on the sample agent workload below.
• Cloudflare's per-token prices for these models match the model makers' own APIs. You pay the same rate and get Cloudflare's network, bindings, and billing.
• The large paid models are limited to 20 requests per minute per model per account (50 with prepaid AI Gateway credits). For many production agent fleets, this limit matters more than the benchmark gap.

All figures below were checked on October 9–11, 2026. Both the catalog and the leaderboard change monthly.

Two catalogs, not one

There are two separate Cloudflare model lists, and they are easy to confuse.

1. Workers AI (69 models). These models run on Cloudflare's own GPUs, have IDs like @cf/zai-org/glm-5.3, and are billed in Neurons.
2. The unified AI catalog (241 models). This list adds third-party models such as Claude, GPT-6, Gemini, Grok, Seedance, and ElevenLabs. Requests go through AI Gateway, the provider runs inference, and you pay with the provider's key or with prepaid Cloudflare credits.

So you can call Claude Opus 5.5 from a Worker, but Workers AI is not serving it. Comparing "Workers AI vs frontier" really means comparing Cloudflare-hosted open-weight models with closed models reachable through the same binding.

What's actually hosted

Here is the Workers AI catalog by task type, taken from the models page (last updated August 12, 2026):

One model in that list has no outside equivalent. Clef is Cloudflare's own 27B multimodal "decision model." You give it a state (text, JSON, or images) and up to 64 typed questions (noul, choice, score), and it returns a probability for every allowed answer. Input costs $0.24 per million tokens and output tokens are free. It is built for routing, triage, and scoring, where people currently ask a chat model for JSON and hope it parses.

The scoreboard

To compare models, I used the Artificial Analysis Intelligence Index. It is currently at v4.3.2 and is a weighted average of 10 evaluations that Artificial Analysis runs itself: agents (30%), coding with Terminal-Bench 4.0 and SciCode (20%), general knowledge and long-context (30%), and scientific reasoning with HLE and CritPt (20%). Full weights are in the methodology page. It is English-only and text-only, and it tells you nothing about speed on Cloudflare specifically. It is still the most consistent independent yardstick available.

Each model is shown at its highest-effort setting:

The open-weight frontier is on Cloudflare, with one exception. GLM-5.3 is the #2 open-weight model on the board, one point behind Xiaomi's MiMo-V2.6-Pro (46), which Workers AI doesn't host. Kimi K3 (44) isn't on Workers AI either, though you can reach it as a third-party model through the unified catalog. Within the open-weight field, Cloudflare is close to the top.

The closed lead is about 13 points, and it is real. A 45 lands around the level of Claude Haiku 5.5 at max effort (43) and GPT-5.6 Terra at max (42). That is a strong model. It is not Opus.

The models people already have in their code are far behind. If your Worker still calls llama-3.3-70b-instruct-fp8-fast, you are running a model that scores 8. Changing one string to glm-5.3-flash gets you to 42.

Two caveats before you trust a column of numbers:

• Effort settings differ. GLM-5.3 defaults to max on Workers AI, so the score above is what you get by default. DeepSeek V4 Pro defaults to high, while the 36 was measured at max. Set reasoning_effort: "max" if you want that number.
• Same weights, different stack. Artificial Analysis benchmarks these models on the makers' own APIs. Cloudflare serves the same weights on its own engine, with its own quantization and kernels. Artificial Analysis lists Cloudflare as a provider only for gpt-oss, Gemma 4, Llama 4 Scout, and Llama 3.3, and publishes no speed figures for it. Treat the hosted scores as very likely, not guaranteed.

What it costs

Workers AI bills in Neurons at $0.011 per 1,000, with 10,000 free Neurons per day on both plans (pricing). For LLMs, Cloudflare also publishes per-token equivalents, so you can ignore Neurons and compare dollars directly.

Cloudflare charges the same per-token rates as the model makers' own APIs.

First-party prices are as reported on the Artificial Analysis model pages for GLM-5.3, GLM-5.3-Flash, and DeepSeek V4 Pro.

For comparison, closed models through the same binding:

Prices come from Anthropic pricing; GPT-6 prices from Artificial Analysis and eesel.ai's summary of OpenAI's pricing page. AI Gateway passes these through with no markup, plus a 5% fee on credit purchases.

A worked example with an agent fleet

List prices mislead for agent workloads because most input tokens are cache hits. So I priced a concrete day:

• 50M input tokens, 80% cache hits
• 2M output tokens
• Uncached input, cached input, and output each billed at the rates above. Models without a published cached price pay full input.

What the table shows:

• GLM-5.3 is only 1.3× cheaper than GPT-6.1 Sol on this workload and scores 7 points lower, because caching narrows the gap. GLM-5.3's cached input costs $0.26 per million on Workers AI, while Sol's costs $0.10 and Opus's costs $0.20. Once 80% of input is cached, the big list-price gap mostly disappears. If your workload is heavily cached, the flagship open model is not the bargain its sticker price suggests.
• GLM-5.3-Flash is the real value story. It scores 42 at $3.70 a day, which is 9× cheaper than GLM-5.3 for 3 fewer points. It is also 5× cheaper than gpt-oss-120b, which has no cached-input discount and scores 12.
• Artificial Analysis's own cost to run the full index tells the same story: $5.98 for Opus 5.5, $2.01 for GLM-5.3, and $0.25 for GLM-5.3-Flash. Per point of index score, Flash comes out about 17× ahead of Opus.

Cloudflare's own numbers point the same way. When it launched Kimi K2.5 in March, Cloudflare said an internal security-review agent processing over 7 billion tokens a day would have cost about $2.4M a year on a mid-tier proprietary model, and that moving it to Workers AI cut costs by 77%. That is a vendor estimate, but the direction matches the table.

Why Cloudflare can serve trillion-parameter models

A year ago, a 1T-parameter model on Workers AI would have sounded like a mistake. A few infrastructure changes made it workable:

• Infire, a Rust inference engine that replaced vLLM in Cloudflare's stack. In Cloudflare's August 2025 benchmark on an H100 NVL, it served 17,224 tokens/s against 16,164 for vLLM 0.10.0 at 25% CPU instead of 140%. The CPU savings matter most because Cloudflare can run it bare-metal instead of under gVisor.
• Prefill/decode disaggregation. Prompt processing and token generation run on separate servers behind a token-aware load balancer. Cloudflare reports p90 inter-token latency falling from about 100 ms to 20–30 ms.
• Session-affinity prompt caching. Send an x-session-affinity header and your requests stick to the node holding your KV cache. Mooncake moves cache between nodes and spills it to NVMe. Internal heavy users raised their cache hit rate from 60% to 80% at peak.
• Speculative decoding with NVIDIA's EAGLE-3 draft model, which helps most on predictable output like tool calls and JSON.
• Unweight, Cloudflare's lossless weight compression, which it says cuts model size by about 15–22%.

Kimi K2.5 alone is about 560 GB of weights and needs at least eight H100s just to load. Cloudflare says it now starts in under 20 seconds and runs about 3× faster than at launch.

The fine print that bites

Rate limits

This is the one most likely to stop a project. Workers AI's general LLM limit is 300 requests per minute. The paid-only models (GLM-5.2, GLM-5.3, GLM-5.3-Flash, Kimi K2.6, Kimi K2.7 Code, DeepSeek V4 Flash, and DeepSeek V4 Pro) are limited to 20 requests per minute per model per account, or 50 if you pay through AI Gateway credits. That is at most 28,800 requests a day at 20 RPM. That is fine for a coding agent but limiting for a consumer chat app. Higher limits require a form.

Paid plan only

Those same models require Workers Paid or prepaid Gateway credits. The free 10,000 Neurons per day is worth $0.11 and is meant for small models.

Context windows aren't always what the model card says

GLM-5.3 and DeepSeek V4 Pro get the full ~1M tokens on Workers AI. But llama-3.3-70b-instruct-fp8-fast is capped at 24,000 tokens, compared with 128k upstream, and its default max_tokens is 256. Check the model page, not the Hugging Face card.

You can't turn GLM-5.3's reasoning off

It supports low, high, and max. Legacy values like none and medium are silently mapped to max, which is the most expensive setting. If you are porting code that sends reasoning_effort: "none", your bill will go up.

Models get retired

Cloudflare deprecated 18 models on May 30, 2026. kimi-k2.5 became an alias for the more expensive K2.6. Pin versions where Cloudflare offers dated IDs (-0813, -0731) and watch the changelog.

What I'd actually build

The setup that makes sense today is a router. Start on the cheap hosted model and escalate to a frontier model when needed, through the same env.AI binding:

export interface Env {
  AI: Ai;
}

export default {
  async fetch(req: Request, env: Env): Promise<Response> {
    const { prompt, hard } = await req.json<{ prompt: string; hard?: boolean }>();

    // Default: Cloudflare-hosted, index 42, $0.15 / $0.50 per 1M tokens
    if (!hard) {
      const r = await env.AI.run("@cf/zai-org/glm-5.3-flash", {
        messages: [{ role: "user", content: prompt }],
      });
      return Response.json(r);
    }

    // Escalation: Claude Opus 5.5 via the unified catalog, index 58
    const r = await env.AI.run("anthropic/claude-opus-5.5", {
      max_tokens: 2048,
      messages: [{ role: "user", content: prompt }],
    });
    return Response.json(r);
  },
} satisfies ExportedHandler<Env>;

Clef is a natural fit for deciding hard. It returns calibrated probabilities instead of free text, and output tokens are free.

My picks by job:

• glm-5.3-flash is my default workhorse. It scores 42, costs very little, has 1M context, and accepts images.
• glm-5.3 handles the hardest open-weight jobs, but price it against GPT-6.1 Sol if your workload is heavily cached.
• clef or clef-flash handles classification, routing, and triage.
• Opus 5.5 or GPT-6.1 Sol through AI Gateway covers anything at the frontier. You still get one binding and one bill.
• Retire the Llama 3.x and gpt-oss calls in existing code now. For general reasoning, they are a generation behind.

The gap between Workers AI and the frontier is about 13 index points. The gap between the Workers AI models people use and the ones Cloudflare now offers is about 34. For most teams, the second gap is the one to close first.

Method

• The catalog comes from the Workers AI models page, all 69 entries extracted and de-duplicated.
• Scores come from the Artificial Analysis leaderboard, Intelligence Index v4.3.2, checked October 9 and 11, 2026. Scores from older index versions were not mixed in. Kimi K2.6, GLM-5.2, and DeepSeek V4 Flash 0731 are hosted on Workers AI but not on the current board.
• Prices come from Cloudflare's pricing page (updated October 9, 2026), Anthropic's pricing docs, and Artificial Analysis model pages. GPT-6 prices were confirmed against a second source that cites OpenAI's page. Gemini 4 Argon's price was only available from Artificial Analysis, so it is left out of the cost tables.
• All ratios and the workload table come from a script, not mental math.
• I didn't measure latency or throughput on Cloudflare's network, or the quality of the image and speech models.

Sources

• Workers AI models · Unified AI catalog · Workers AI pricing · Workers AI limits
• GLM-5.3 on Workers AI · GLM-5.3-Flash on Workers AI · Planned model deprecations
• AI Gateway unified billing
• Powering the agents: Workers AI now runs large models · Building the foundation for running extra-large language models · Infire · InfoQ coverage
• Artificial Analysis leaderboard · Methodology · Provider leaderboard
• Anthropic API pricing · GPT-6 Astra pricing (eesel.ai)