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Claude Instant vs WizardLM-2 8x22B

This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.

Anthropic

Model

Claude Instant

Context window

100K

100,000 tokens · ~75K words

Model page
Microsoft

Model

WizardLM-2 8x22B

Context window

66K

65,536 tokens · ~49K words

Model page

Context window · side by side

Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.

Claude Instant100K
WizardLM-2 8x22B66K

Claude Instant has about 1.5× the context window of the other in this pair.

Claude Instant has 52% more context capacity (100K vs 65K tokens). WizardLM-2 8x22B is 40% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Claude Instant. Its 100K context fits entire documents without chunking (vs 65K).

  • RAG / high-volume retrieval

    Use WizardLM-2 8x22B. Input tokens are 40% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use WizardLM-2 8x22B. Its 65K max output lets you generate complete artifacts in one request.

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecClaude InstantWizardLM-2 8x22B
Context window100,000 tokens (100K)65,536 tokens (65K)
Max output tokens8,191 tokens (8K)65,536 tokens (65K)
Speed tierBalancedBalanced
VisionNoNo
Function callingNoNo
Extended thinkingNoNo
Prompt cachingNoNo
Batch APIYesNo
Release dateN/AApr 2024

Pricing matrix

Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.

ProviderClaude Instant inClaude Instant outWizardLM-2 8x22B inWizardLM-2 8x22B out
Aws Bedrock$0.800/M$2.40/M
Deepinfra$0.480/M$0.480/M
Novita$0.620/M$0.620/M

Frequently asked questions

Claude Instant has a larger context window: 100K tokens vs 65K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

Powered by Mem0

Use a smaller model.
Get better results.

Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.

Example: a multi-turn chat session

Without Mem0~128K tokens sent
Full history
Repeated info
Old context
With Mem0~20K tokens sent
Key memories
Current turn

80% less to send — works with any model