Ranked · Best for

Best long-context model APIs, ranked

GLM-5.3’s 1M-token context window fits whole codebases, contract sets, or literature bundles in a single call — verify retrieval and citation accuracy on your own documents before relying on it.

Ranked by a stated method

Ranking combines the Artificial Analysis Intelligence Index, task-specific evaluation results where independently reproduced, and price per million tokens. Vendor-only claims are excluded from the ranking itself and shown separately where relevant.

Candidates

ModelWhy it ranks hereFull comparison
GLM-5.31M-token context at a fraction of closed-frontier price; verify citation accuracy per use case.View →
Claude (Fable 5 / Opus)Strong long-context reasoning; higher price per token.Compare →
Gemini 3.1 ProLong context plus multimodal document input.Compare →

This is not a universal winner

A ranked list compresses many workloads into one order. Check the linked comparison for your specific workload before deciding — especially where modality support or self-host requirements are the deciding factor.

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Muse Spark 1.3: long-context reference

The supplied 3 September 2026 research records a 1,048,576-token input window, with launch-scorecard MRCR results of 98.5 at 256K–512K and 98.1 at 512K–1M for the gated max tier. These retrieval scores do not guarantee task accuracy. Muse Spark 1.3 is closed weights and is not served through OneInfer.

ResourceLink
Muse Spark 1.3 benchmarksView →
Muse Spark family directoryView →

Frequently asked questions

Where is the live GLM-5.3 model page?

The canonical model page with current OneInfer pricing, capabilities, and availability is /models/zai-org/GLM-5.3. This page is a focused facet of that entity, not a replacement for it.

How should I treat benchmark or price claims?

Check each claim’s provenance label and observed date. Vendor-reported and independently verified numbers are shown as separate evidence classes on this hub.

Put GLM-5.3 to work

Fund a controlled evaluation, start with a prepared prompt, and measure quality and cost on your own workload.