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Fireworks AI · none caching

Qwen3 VL 235B A22B Instruct

Qwen3 VL 235B A22B Instruct on Zumik: live pricing, context, and caching, routable by id or alias through one OpenAI-compatible endpoint.

Call it through ZumikAbout Fireworks AI
$0.30
Input / 1M tokens
$1.50
Output / 1M tokens
$0.30
Cache read
131K
Context window

Specifications

At a glance.

ProviderFireworks AI
Familyqwen3_vl_moe
Released2025-09
LicenseOpen weights
Context window131K tokens
Max output33K tokens
Parameters236B
Modalitiestext, image, video
Tool callingYes
Reasoning modeNo
Cachingnone
Batch discountNo batch tier

Measured by Zumik

What reuse looks like here.

Not yet profiled

Pricing, context, and capabilities for Qwen3 VL 235B A22B Instruct are live, but it is outside the flagship set Zumik benchmarks in depth, so measured reuse, capture, and warm TTFT are not shown yet. Run a workload estimate or route it by id to start collecting traces.

Reuse economics

What you actually pay once caching works.

At a typical 55% prefix reuse, a million input tokens on Qwen3 VL 235B A22B Instruct effectively costs $0.30 instead of $0.30 - blending to roughly $0.60 with a 25% output share. There is no batch tier, so cost control here leans on caching and routing.

Estimate it for your workload
Best for
textimagevideo

Route it directly by id, or let an alias pick it when it wins under policy.

Call it

Same OpenAI client, this model.

python
from openai import OpenAI

client = OpenAI(base_url="https://api.zumik.ai/v1", api_key="zk_live_...")

r = client.responses.create(
    model="qwen3-vl-235b-a22b-instruct",
    input="Draft a fix for the failing test.",
)
print(r.usage.input_tokens_cached)   # confirm reuse

Frequently asked

Qwen3 VL 235B A22B Instruct, answered.

How much does Qwen3 VL 235B A22B Instruct cost?

Qwen3 VL 235B A22B Instruct is $0.30 per million input tokens and $1.50 per million output tokens through Zumik. Cache reads are $0.30 per million, a 0% discount on input.

What is Qwen3 VL 235B A22B Instruct's context window?

Qwen3 VL 235B A22B Instruct supports a 131K-token context window with up to 33K output tokens.

Does Qwen3 VL 235B A22B Instruct support prompt caching?

Yes. Fireworks AI uses Automatic prompt caching (serverless and dedicated) caching. In the Zumik corpus, Qwen3 VL 235B A22B Instruct shows a median cache capture of 81% on agent workloads.

Run Qwen3 VL 235B A22B Instruct with reuse measured.

Point an OpenAI client at Zumik and see exactly how much of this model's input you are reusing.

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