Requesty
Fireworks AI

qwen3.6-plus

Qwen 3.6 Plus is Alibaba's latest flagship closed model, available exclusively through Fireworks AI outside of Alibaba's own infrastructure. Please contact Fireworks AI to get dedicated instances for Qwen 3.6 Plus.

VisionTool callingCaching
Try qwen3.6-plusCompareDocs

Specifications

Context window131K tokens
Max output4K tokens
API typechat
AddedApr 29, 2026
Model IDfireworks/qwen3.6-plus
Data retentionNo
Used for trainingNo
Provider location🇺🇸 US
Privacy policyFireworks Privacy Policy→

Benchmarks

Released 2026-04-02
Coding Indexcoding
54.5%

Artificial Analysis Coding Index — a composite of coding evaluations including LiveCodeBench, SciCode and Terminal-Bench.

GPQA Diamondreasoning
88.2%

Graduate-level physics, chemistry & biology questions designed to resist Googling.

Intelligence Indexreasoning
39.6%

Artificial Analysis Intelligence Index — a composite of multiple evaluations measuring overall model capability.

Scores are sourced from official model cards, Artificial Analysis, and public leaderboards. Benchmarks measure specific skills and do not capture every aspect of model quality. Always test on your own workload.

Pricing

Input / 1M
$0.50
Output / 1M
$3.00
Cache write
N/A
Cache read / 1M
$0.10
Estimated cost
100K input + 10K output$0.0800
1M input + 100K output$0.80
10M input + 1M output$8.00

Requesty charges exactly what the upstream provider charges, no markup, no per-request fees. Prompt caching and smart routing can reduce effective cost by 30-80%.

Quickstart

Drop-in compatible with the OpenAI SDK. Change the base URL, swap in your Requesty API key, and set the model to fireworks/qwen3.6-plus.

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from openai import OpenAI

client = OpenAI(
    api_key="YOUR_REQUESTY_API_KEY",
    base_url="https://router.requesty.ai/v1",
)

response = client.chat.completions.create(
    model="fireworks/qwen3.6-plus",
    messages=[
        {"role": "user", "content": "Explain quantum computing in one paragraph."},
    ],
)

print(response.choices[0].message.content)

Other Fireworks AI models

glm-5.2
1M ctx$1.40/1M
Coding 51
qwen3.7-plus
262K ctx$0.40/1M
Coding 56
nemotron-3-ultra-nvfp4
262K ctx$0.60/1M
minimax-m3
512K ctx$0.30/1M
Coding 59
kimi-k2.7-code
262K ctx$0.95/1M
Coding 61
gpt-oss-120b
131K ctx$0.15/1M
Coding 30

Frequently asked questions

How much does qwen3.6-plus cost?
qwen3.6-plus is priced at $0.50 per million input tokens and $3.00 per million output tokens when accessed via Requesty. Prompt caching is supported, which can cut effective input cost by up to 90% on repeated context. Requesty charges exactly what the upstream provider charges, we don't add markup.
What is the context window of qwen3.6-plus?
qwen3.6-plus has a context window of 131K tokens, with a maximum output of 4K tokens per response. That's roughly 175 words of input you can fit in a single prompt.
How does qwen3.6-plus perform on benchmarks?
qwen3.6-plus scores 97.7% on τ²-Bench, 88.2% on GPQA Diamond, 54.5% on Coding Index. See the full benchmark chart above for results across MMLU Pro, GPQA Diamond, SWE-Bench Verified, HumanEval, MATH, AIME, MMMU, and LiveBench.
What can qwen3.6-plus do?
qwen3.6-plus supports vision input, tool calling, prompt caching. You can call it through any OpenAI-compatible client by pointing base_url to Requesty.
How do I use qwen3.6-plus with the OpenAI SDK?
Install the OpenAI SDK, set base_url to "https://router.requesty.ai/v1", set your API key to your Requesty key, and set the model to "fireworks/qwen3.6-plus". The Quickstart above shows Python, JavaScript and cURL snippets.
Can I run qwen3.6-plus through Requesty?
Yes. qwen3.6-plus runs through Requesty's OpenAI-compatible API, served from Fireworks AI. You do not host the model yourself: point base_url at Requesty, set the model to "fireworks/qwen3.6-plus", and requests are routed to the upstream provider with automatic failover. The same key gives you 400+ other models too.

Access qwen3.6-plus through Requesty

One API key, 400+ models, OpenAI-compatible. No markup on provider prices, automatic failover, and smart caching built-in.

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