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Anthropic Claude 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.
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.
Anthropic Claude has about 1.5× the context window of the other in this pair.
Anthropic Claude has 52% more context capacity (100K vs 65K tokens). WizardLM-2 8x22B is 94% cheaper on input.
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use Anthropic Claude. Its 100K context fits entire documents without chunking (vs 65K).
RAG / high-volume retrieval
Use WizardLM-2 8x22B. Input tokens are 94% 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.
| Spec | Anthropic Claude | WizardLM-2 8x22B |
|---|---|---|
| Context window | 100,000 tokens (100K) | 65,536 tokens (65K) |
| Max output tokens | 8,191 tokens (8K) | 65,536 tokens (65K) |
| Speed tier | Balanced | Balanced |
| Vision | No | No |
| Function calling | No | No |
| Extended thinking | No | No |
| Prompt caching | No | No |
| Batch API | Yes | No |
| Release date | N/A | Apr 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.
| Provider | Anthropic Claude in | Anthropic Claude out | WizardLM-2 8x22B in | WizardLM-2 8x22B out |
|---|---|---|---|---|
| Aws Bedrock | $8.00/M | $24.00/M | — | — |
| Deepinfra | — | — | $0.480/M | $0.480/M |
| Novita | — | — | $0.620/M | $0.620/M |
Frequently asked questions
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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
80% less to send — works with any model