Qwen3 4B
novita-ai/qwen3-4bمن novita-ai · أُصدِر 2025-04-29
⚠ هذا نموذج مُحسَّن من المجتمع أو مشتق — وليس إصدارًا رسميًا من المزود.
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
القدرات
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.
Coding13
- Tool calling0/40
- Structured output0/20
- Reasoning10/10
- Context window (100K → 1M)2/20
- Provider availability1/10
Agents28
- Tool calling0/35
- Structured output0/25
- Reasoning15/15
- Output token limit12/15
- Provider availability1/10
JSON / structured output30
- Structured output / JSON mode0/50
- Tool calling0/20
- Temperature control10/10
- Price-friendly for high-volume20/20
Cost efficiency91
- Headline price (log-scaled)91/95
- Has prompt-cache pricing0/5
Long context45
- Context window (100K → 2M)35/90
- Has published price for full window10/10
Production-readiness50
- Number of independent providers5/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers10/10
- Official model (not derivative)0/15
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 | $0.17 < $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 | $0.33 < $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 | $0.07 < $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 | $0.27 < $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 | $0.38 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
تفاصيل التسعير
السعر المُوصى به من novita-ai · qwen/qwen3-4b-fp8
متاح لدى 1 مزود
| المزود | معرف نموذج المزود | إدخال / 1M | إخراج / 1M | السياق | تاريخ الإصدار |
|---|---|---|---|---|---|
| NovitaAI novita-ai | qwen/qwen3-4b-fp8 | $0.030 | $0.030 | 128K | 2025-04-29 |
Frequently asked questions
How much does Qwen3 4B cost?
Qwen3 4B costs $0.030 per 1M input tokens and $0.030 per 1M output tokens, sourced from novita-ai. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Qwen3 4B?
Qwen3 4B has a context window of 128K tokens, with a max output of 20K tokens per reply. This is the total combined size of prompt + completion.
Does Qwen3 4B support tool calling?
No. Qwen3 4B does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.
Does Qwen3 4B support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Qwen3 4B in our data source. Verify with novita-ai's official documentation before relying on it in production.
Can Qwen3 4B accept image input?
No. Qwen3 4B only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Qwen3 4B open-weight?
Yes. Qwen3 4B'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 Qwen3 4B?
If Qwen3 4B doesn't fit, consider Ling-2.6-1T, Ling-2.6-flash, PaddleOCR-VL. 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 come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
Explore more
More novita-ai models
- Ling-2.6-1TUnknown pricing
- Ling-2.6-flash$0.10 in / $0.30 out
- PaddleOCR-VL$0.02 in / $0.02 out
- baichuan-m2-32b$0.07 in / $0.07 out
- Mythomax L2 13B$0.09 in / $0.09 out
Capability lists this model is in
آخر تحديث:
Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.