Requesty

qwen3.8-2.4t-a95b

TensorX Ltd./🇪🇺 EU/chat

Qwen3.8 2.4T A95B is an open-weight sparse mixture-of-experts model from Qwen and the open-weight variant of Qwen3.8 Max, with 95 billion active parameters out of 2.4 trillion total. It is suited for coding, research, complex reasoning, and agentic workflows.More
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Which id to call

tensorx/qwen3.8-2.4t-a95b

This exact deployment on TensorX Ltd., with no routing and no failover. Send it as the model field.

Input /1M

$2.50

$0.63 cached

Output /1M

$6.00

2.4x input

Context

1M

262K output

Added

Aug 2026

chat

Capabilities 5/8

VisionReasoningTool callingCachingWeb searchJSON schemaComputer useImage generation

Provider rates

What qwen3.8-2.4t-a95b costs

Provider prices per 1M tokens, updated August 27, 2026.more

These are the upstream provider rates. Pay as you go adds 5%, or 0% if you bring your own keys, and there is no per-request fee. Prompt caching and routing change what you pay against these rates, not the rates themselves.

Input /1M

$2.50

Output /1M

$6.00

Cache write /1M

-

Cache read /1M

$0.63

What a workload costs

100K input + 10K output
$0.31
1M input + 100K output
$3.10
10M input + 1M output
$31.00

At the rates above, before caching. A cache read costs $0.63 per 1M, so repeated context lands under these figures.

OpenAI compatible

Call it in three lines

Change the base url, use your Requesty key, set the model to tensorx/qwen3.8-2.4t-a95b. Existing OpenAI SDK code needs no other edit.

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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="tensorx/qwen3.8-2.4t-a95b",
    messages=[
        {"role": "user", "content": "Explain quantum computing in one paragraph."},
    ],
)

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

Reference

Specs and data terms

What the catalog reports for this deployment, and what the provider does with the traffic.

Context window1M tokens
Max output262K tokens
API typechat
AddedAug 2026
Model id
Data retentionNone
Used for trainingNo
Served from🇪🇺 EU
Privacy policyTensorX Privacy Policy→

Released 2026-08-12

Benchmark scores

Benchmarks measure the model, not this endpoint, so they are the same wherever these weights are served.

Coding Indexcoding
71.9%

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

GPQA Diamondreasoning
93.5%

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

Intelligence Indexreasoning
57.7%

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

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

Same provider

More from TensorX Ltd.

Newest first, on the same provider and the same key.

qwen3.8

1M ctx/$2.50/1M

deepseek-v4-flash-0731

1.0M ctx/$0.25/1M

kimi-k3

1.0M ctx/$3.00/1M

glm-5.2

1.0M ctx/$1.50/1M

kimi-k2.7-code

262K ctx/$1.25/1M

minimax-m3

1.0M ctx/$0.40/1M

Reference

qwen3.8-2.4t-a95b questions

How much does qwen3.8-2.4t-a95b cost?

qwen3.8-2.4t-a95b is priced at $2.50 per million input tokens and $6.00 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. Those are the upstream provider rates: pay as you go adds 5% on top, or 0% if you bring your own provider keys.

What is the context window of qwen3.8-2.4t-a95b?

qwen3.8-2.4t-a95b has a context window of 1M tokens, with a maximum output of 262K tokens per response. That's roughly 1,333 words of input you can fit in a single prompt.

How does qwen3.8-2.4t-a95b perform on benchmarks?

qwen3.8-2.4t-a95b scores 93.5% on GPQA Diamond, 71.9% on Coding Index, 57.7% on Intelligence 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 qwen3.8-2.4t-a95b do?

qwen3.8-2.4t-a95b supports vision input, tool calling, extended reasoning, prompt caching, structured outputs (JSON schema). You can call it through any OpenAI-compatible client by pointing base_url to Requesty.

How do I use qwen3.8-2.4t-a95b 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 "tensorx/qwen3.8-2.4t-a95b". The Quickstart above shows Python, JavaScript and cURL snippets.

Can I run qwen3.8-2.4t-a95b through Requesty?

Yes. qwen3.8-2.4t-a95b runs through Requesty's OpenAI-compatible API, served from TensorX Ltd.. You do not host the model yourself: point base_url at Requesty, set the model to "tensorx/qwen3.8-2.4t-a95b", and requests are routed to the upstream provider with automatic failover. The same key gives you 600+ other models too.

Call qwen3.8-2.4t-a95b through one endpoint

One key for this endpoint and 600+ other models. No markup on provider prices, automatic failover, caching built in.

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