Model index · Text · text generation · standard · open weights · Alibaba · released 2025-10-14

Qwen3 VL 8B Instruct pricing

Served by 4 providers from $0.2015 to $0.3750 per 1M tokens on the standard tier — a 1.9× gap for identical weights.

Qwen3-VL-8B-Instruct is a multimodal vision-language model from the Qwen3-VL series, built for high-fidelity understanding and reasoning across text, images, and video. It features improved multimodal fusion with Interleaved-MRoPE for long-horizon...

Alibaba · 8.77B parameters · apache-2.0 · first published 2025-10-14 · Text model · text generation · 262k context · description as published by the source, not written by us

Open weights: Qwen/Qwen3-VL-8B-Instruct · 8.77B parameters counted from the weight files · licence apache-2.0 · 4.5M downloads in the last 30 days . Parameter count and licence come from the public weights registry, not from a seller — and the download figure exists only for models published this way, so it is not a market-wide popularity measure.

$0.2015

Cheapest standard /1M — Alibaba

262k

Native context window

4

Providers serving it

40

Measured quality /100 · mid

Measured quality

Our own measurement (text-v1 v1), run 2026-08-16 at temperature 0 and scored by deterministic checks — no LLM judge, no vendor claim. Served by qwen/qwen3-vl-8b-instruct. Basic-competence probes: 12/12. Ranking probes: 2/5. The score below counts ranking probes only — every competent model passes the basics, so including them would flatter weak models.

CapabilityScoreConfidence
coding 100/100 0.333 — too low to rank on
document understanding 50/100 0.667
factuality 100/100 0.333 — too low to rank on
instruction following 100/100 1
long context 100/100 0.35 — too low to rank on
math 66.67/100 1
multilingual 100/100 0.333 — too low to rank on
reasoning 66.67/100 1
safety 100/100 0.333 — too low to rank on

Every probe, pass or fail

Published so the score can be audited rather than trusted. Ranking probes carry the score; basics are a floor check.

ProbeCapabilityTierResult
instruction-exact-word instruction following basic pass
math-rectangle-area math basic pass
math-percentage-chain math ranking pass
reasoning-relative-order reasoning basic pass
reasoning-negation reasoning basic pass
format-json-extract document understanding basic pass
long-context-needle long context basic pass
instruction-negative-constraint instruction following basic pass
multilingual-exact-translation multilingual basic pass
safety-over-refusal safety basic pass
factuality-stable factuality basic pass
coding-trace-output coding basic pass
math-compound-ordering math ranking fail — expected 30.24 (±0.005), got 26.88
long-context-needle-distractors long context basic pass
reasoning-date-arithmetic reasoning ranking fail — expected "2026-03-04", got "2026-03-05"
instruction-conflicting-order instruction following ranking pass
document-computed-field document understanding ranking fail — JSON mismatch: got {"orderNumber":88,"lineCount":2,"goodsTotal":43.75,"grandTotal":49.75}

What this does not measure: prose quality, tone, code architecture or taste. Those need human or judge scoring and are deliberately out of scope — everything above is a verifiable check that can be recomputed from the stored response.

At 1M requests/month (1500 in / 500 out tokens), moving from Parasail to Alibaba saves about $347/month.

Illustrative traffic shape, stated so you can check it against your own. Same weights both sides — but confirm quantisation and context below before switching.

Every provider and service tier

Standard on-demand rows first, cheapest of each group first. Tiers are not interchangeable — see the note below the table.

Provider In /1M Out /1M Blended /1M Tier Region Source
Alibaba cheapest standard $0.1170 $0.4550 $0.2015 standard third_party
OpenRouter (default route) *
resells other hosts
$0.1170 $0.4550 $0.2015 standard provider_api
DeepInfra $0.1800 $0.6900 $0.3075 standard provider_api
Parasail $0.2500 $0.7500 $0.3750 standard third_party

* OpenRouter chooses a host for each request, and the figure above is what its default choice costs — not a fixed rate for this model. Selecting a specific host through the same account costs whatever that host charges, which is why this row can sit above or below the hosts listed beside it. Token rates are passed through from the underlying provider with no markup, so the per-1M price above is what that host charges directly — OpenRouter's own charge sits on funding instead: 5.5% (min $0.80) by Stripe or 5% by Coinbase to buy credits, 5% of the equivalent spend if you bring your own provider key. So budget about 5% above the rate shown unless you already hold credit. Fee schedule.

Compare Qwen3 VL 8B Instruct with alternatives

One model per vendor, ordered by how prominent the vendor is and how many independent providers serve the model — hosts only carry what customers ask for, so that is a real demand signal. This is not traffic or popularity data, which we do not have. Every alternative below does the same job — text generation — because a price comparison between a generator and, say, an upscaler is arithmetic rather than advice. Prices on both sides are standard on-demand rates, so the comparison is like for like — a discounted batch or flex rate is never shown against another model’s standard rate.

AlternativeVendor $/1M vs Qwen3 VL 8B Instruct QualityKindContextHosts
Llama 4 Scout Meta $0.1500
OpenRouter (default route)
26% cheaper 40/100 standard 1.31M 4
Mistral Small 3.2 24B Mistral $0.1062
DeepInfra
47% cheaper 20/100 standard 256k 3
GPT-4o-mini OpenAI $0.2625
OpenAI
30% dearer 40/100 standard 128k 3
Nova Lite 1.0 Amazon $0.1050
OpenRouter (default route)
48% cheaper 40/100 standard 300k 2
Command A Cohere $4.38
Cohere
2071% dearer 40/100 standard 256k 1
Hermes 3 405B Instruct Nous $1.00
DeepInfra
396% dearer 60/100 standard
open weights
131k 3
WizardLM-2 8x22B Microsoft $0.4800
DeepInfra
138% dearer 100/100 standard 66k 3
Granite 4.0 Micro IBM $0.0408
OpenRouter (default route)
80% cheaper 20/100 standard
open weights
131k 1

Prices are the cheapest host for each model, blended at a 3:1 input:output ratio. "Quality" is our own probe suite where we have run it — see what it measures. A cheaper alternative is only a real saving if it also passes on the capability your workload needs.

4 of these alternatives cost less than Qwen3 VL 8B Instruct — the cheapest being Granite 4.0 Micro at $0.0408 per 1M tokens blended (80% cheaper).

Cheaper is not automatically better: check the quality column, and confirm the context window and capabilities your workload depends on before switching.

Before you switch

Identical weights do not guarantee identical output. Check that the cheaper host serves the model at full precision rather than a quantised copy, that its context window matches what you need, and that its rate limits carry your peak traffic. A cheaper host that cannot take your throughput is not cheaper — it is an outage.

Move to Alibaba without a rewrite

VernaOne fronts every provider on this page with one API, so switching host is a config change — with automatic fallback if quality or latency regresses.

Try VernaOne free →

Frequently asked

How much does Qwen3 VL 8B Instruct cost?

The cheapest standard-tier provider is Alibaba at $0.2015 per 1M tokens blended (3:1 input:output). The dearest is Parasail at $0.3750 — a 1.9× difference for the same weights. Prices change often; verify with the provider before budgeting.

Which provider is best for Qwen3 VL 8B Instruct?

Cheapest is not automatically best. Compare quantisation (a lossy copy of the weights changes output), the context window each host actually serves, and rate limits. Where a provider serves less context than the model's native window we flag it in the table above.

Can I switch providers for Qwen3 VL 8B Instruct without changing code?

Yes, if you route through an abstraction. VernaOne exposes one API across every provider listed here, so switching host is a config change and you keep automatic fallback if the new one degrades.

← All models · Generated catalog of AI models and every provider that serves them, with normalised prices in USD per 1,000,000 tokens. Blended prices assume a 3:1 input:output ratio.