WizardLM 2 7B
1931°
<wizard2-7b>
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microsoft | 32,768 token context | $0.06/M input, $0.06/M output. WizardLM-2 7B is the smaller variant of Microsoft AI's latest Wizard model. It is the fastest and achieves comparable performance with existing 10x larger open-source leading models
Captain's notes
Notes from 2026-10-13
WizardLM 2 7B is Microsoft AI's compact entry in the WizardLM 2 family, built to punch above its weight class. Microsoft claims it reaches performance comparable to open-source models roughly ten times its size, which is a bold claim for a 7B. At $0.06 per million tokens in and out, it suits developers who need a capable, cheap model for everyday tasks without paying frontier prices.
Known for
- Competitive performance against much larger open-source models, per Microsoft
- Very low API cost: $0.06 per million tokens each way
- Fast inference typical of 7B-class models
- 32,768-token context window covers most practical tasks
Watch out for
- Weights are not publicly available; you are tied to the API, no local deployment.
- Microsoft has not published detailed benchmark numbers or a public model card.
- A 7B model will still hit limits on complex multi-step reasoning or long document work.
Prompts to try
Quick document summary
Here is a meeting transcript. Summarise the key decisions made, the owners assigned to each action item, and any unresolved questions. Keep it under 200 words and use bullet points.
[paste transcript here]
Explain a code bug
I have a Python function that is supposed to return the top three most frequent words in a string, but it returns them in the wrong order. Here is the code:
def top_words(text):
from collections import Counter
words = text.split()
return Counter(words).most_common()[:3]
What is wrong and how do I fix it?
Rewrite for clarity
Rewrite the following paragraph so it is clear to a non-technical reader. Cut jargon, keep every factual claim, and aim for sentences under 20 words.
The asynchronous event-driven architecture leverages non-blocking I/O primitives to maximise throughput under high-concurrency workloads while minimising latency variance across distributed service endpoints.
Compare with
- GPT-5.6 LunaAPI-only model1,050,000 token context · $0.20/M input, $1.20/M output
- GPT-4o-miniAPI-only model128,000 token context · $0.15/M input, $0.60/M output
- SonarAPI-only model127,072 token context · $1.00/M input, $1.00/M output
- GPT-3.5 Turbo (older v0613)API-only model4,095 token context · $1.00/M input, $2.00/M output
More text models from microsoft
Questions people ask
- Can I run WizardLM 2 7B locally?
- No. The weights are not publicly released, so the model is API-only; local deployment is not an option.
- What does WizardLM 2 7B cost and what is the context window?
- Pricing is $0.06 per million input tokens and $0.06 per million output tokens, with a 32,768-token context window.
- How does WizardLM 2 7B compare to the larger WizardLM 2 models?
- It is the smallest and fastest variant in the WizardLM 2 family; Microsoft positions it as comparable to open-source models around ten times its size, though the larger variants are aimed at heavier workloads.
Captain's verdictA genuinely cheap, fast 7B for everyday API use, held back mainly by closed weights and a thin public record.
Captain writes these notes from the model's own description and model card. Prices and context sizes are the provider's at the time of writing and can change.
Ranking badge
Updated Oct 22, 2026
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Paste it into your Hugging Face card or Civitai description.
How this score adds up
This model has no popularity data from any source yet, so it carries a stand-in number below 7,000° that keeps the lists complete. It never ranks above a model with data. The breakdown appears here as soon as a source reports it.
Glossary: the terms used here
- Heat score
- One number for how much a model is used and wanted right now, from 0° to 9,999°. Every list on this site that says hottest is sorted by it and nothing else.
- Floor
- The 7,000° every model with real data starts from. The scale runs from 0° to 9,999°; the part below the floor is reserved for stand-ins (models without data), so that a model with data always ranks above one without. The floor is the same for everyone and decides nothing; the degrees above it are the measurement.
- Download-equivalents
- Everything the score treats as a download, added up: downloads themselves, each torrent seeder as 1,000, each AI Horde image and each run on a generation site as one.
- Log scale
- Counting by tens, not by ones: going from 1,000 to 10,000 downloads adds the same degrees as going from 10,000 to 100,000. It keeps giants from crowding out everything else.
- Likes
- Thumbs up and likes from every source, worth half a download each on the log scale.
- Adoption rate
- How many downloads a day a model gets right now, from the difference between two readings of a source. A quarter of the weight of the download term.
- Measured and estimated
- Measured means two readings of a source exist and the difference is real. Estimated means only today's total is known and the past is spread over the model's life; the site says which one it is showing.
- Seeder
- Someone who keeps a copy of the weights online for others to download over a torrent, or a hosted mirror doing the same. Each one counts as 1,000 downloads.
- AI Horde
- A volunteer network that generates images for free on donated computers. The number of images it has made with a model is public, and each counts as one download.
- Generation sites
- Sites that host their own copies of a model and let people generate with it, such as PixAI, SeaArt and Yodayo. Their downloads count like Civitai's and each generation run as one download.
- OpenRouter tokens
- A text model served through an API, such as Claude or GPT, has no weights to download, so its usage is measured in tokens: the pieces of text it read and wrote through OpenRouter, a gateway many apps use, over the last 30 days. Every 10,000,000 tokens count as one download. Source: OpenRouter (openrouter.ai/rankings), CC BY 4.0.
- Full model (checkpoint)
- The weights themselves, the file that generates on its own. It gets a bonus because add-ons depend on it.
- LoRA and embedding
- Small add-on files that steer someone else's weights toward a style, a character or a concept. They cannot run alone.
- Family and weights
- The base a model is built on, such as SD 1.5, SDXL, Flux or Wan. Every model inherits the release date of its family's weights.
- Age
- Degrees taken off for how long ago the family's weights came out: 150° a year, up to 750°. It keeps old downloads that had years to pile up from freezing the top of the list.
- Family boost
- A small, bounded nudge for LoRAs of a family this site is pushing forward right now, always listed in the breakdown.
- Stand-in score
- A stable number below the floor given to a model no source reports on yet, so lists stay complete. It never ranks above a model with data.
- New! (grace period)
- A model less than 7 days old shows New! instead of a score and holds no place in the rankings. Its score is computed from day one, but a model one day old grows faster than anything, so without the wait the week's #1 would always be whatever came out yesterday rather than what held up for a week.
- Trend
- How a model's growth over a period compares with the typical model's growth over the same period. Faster than typical reads as a rise, slower as a fall.
- Typical model
- The middle of the catalog: half the models with a figure grew faster over the period, half slower.
WizardLM 2 7B is a text model for the Text / LLM family listed on ModelTrends.ai, a read-only catalog of open source AI image, video and text models. Compare it with other Text / LLM models, check its heat score to see how it is trending, and open it on PirateDiffusion or BitVector to try it.
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