WizardLM-2 8x22B
microsoft/wizardlm-2-8x22bОт Microsoft · семейство: gpt · выпуск 2024-04-24 · дата знаний: 2024-04-30
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Возможности
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding4
- Tool calling0/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)0/20
- Provider availability4/10
Agents9
- Tool calling0/35
- Structured output0/25
- Reasoning0/15
- Output token limit5/15
- Provider availability4/10
JSON / structured output28
- Structured output / JSON mode0/50
- Tool calling0/20
- Temperature control10/10
- Price-friendly for high-volume18/20
Cost efficiency63
- Headline price (log-scaled)63/95
- Has prompt-cache pricing0/5
Long context0
- Context ≥ 100K0/100
Production-readiness74
- Number of independent providers20/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers4/10
- Official model (not derivative)15/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $2.71 < $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $5.42 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $1.23 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $4.44 < $0.01 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $6.21 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Детализация цен
Рекомендованная цена от nano-gpt · microsoft/wizardlm-2-8x22b
Доступна у 4 провайдеров
| Провайдер | ID модели провайдера | Вход / 1M | Выход / 1M | Контекст | Выпуск |
|---|---|---|---|---|---|
| OpenRouter openrouter | microsoft/wizardlm-2-8x22b | $0.620 | $0.620 | 66K | 2024-04-16 |
| NovitaAI novita-ai | microsoft/wizardlm-2-8x22b | $0.620 | $0.620 | 66K | 2024-04-24 |
| Kilo Gateway kilo | microsoft/wizardlm-2-8x22b | $0.620 | $0.620 | 66K | 2024-04-24 |
| NanoGPT nano-gpt | microsoft/wizardlm-2-8x22b | $0.493 | $0.493 | 66K | 2025-04-15 |
Расхождения данных между провайдерами
- context_window varies: 65535, 65536
- release_date varies (span 364d): 2024-04-16, 2024-04-24, 2025-04-15
- modalities varies across offerings
Провайдеры сообщают разные значения для этой модели. Сводка выше использует репрезентативного провайдера; детали — в таблице.
Frequently asked questions
How much does WizardLM-2 8x22B cost?
WizardLM-2 8x22B costs $0.493 per 1M input tokens and $0.493 per 1M output tokens, sourced from nano-gpt. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of WizardLM-2 8x22B?
WizardLM-2 8x22B has a context window of 66K tokens, with a max output of 8K tokens per reply. This is the total combined size of prompt + completion.
Does WizardLM-2 8x22B support tool calling?
No. WizardLM-2 8x22B does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.
Does WizardLM-2 8x22B support structured output / JSON mode?
No. WizardLM-2 8x22B does not support structured output / JSON-schema-constrained decoding. If your workflow requires it, look at the /capabilities/structured-output list for alternatives.
Can WizardLM-2 8x22B accept image input?
No. WizardLM-2 8x22B only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is WizardLM-2 8x22B open-weight?
Yes. WizardLM-2 8x22B's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.
What are the best alternatives to WizardLM-2 8x22B?
If WizardLM-2 8x22B doesn't fit, consider Phi 4 Mini Instruct, Phi-4, Phi-3.5-MoE-instruct. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers are normalised into a single canonical model record and reconciled with each provider's official documentation. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
Explore more
More Microsoft models
- Phi 4 Mini Instruct$0.08 in / $0.35 out
- Phi-4$0.06 in / $0.14 out
- Phi-3.5-MoE-instruct$0.16 in / $0.64 out
- Phi-3-small-instruct (128k)$0.15 in / $0.60 out
- Phi-3.5-mini-instruct$0.13 in / $0.52 out
Capability lists this model is in
Последнее обновление:
Pricing and capabilities are refreshed daily and reconciled against each provider's official documentation. Always verify critical production decisions with the provider directly.