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Qwen3.5 122B-A10B

alibaba·alibaba/qwen3.5-122b-a10b·Deprecated·Open weights·qwen series
Qwen vision-language model for visual reasoning, documents, and agent tasks

Qwen3.5 122B-A10B by alibaba is offered by 23 providers on this page. Public prices are shown for 22 of them. Its official list price is $0.40 per 1M input tokens and $3.20 per 1M output tokens. The lowest paid channel is AIHubMix at $0.113 / $0.901 per 1M, about 4.4× below the list price. That channel is a third-party gateway, so confirm its availability and rate limits before depending on it. It also has 1 free ($0) channel; free tiers usually carry rate limits, and subscription-covered access bills $0 per token only after the subscription fee.

Artificial Analysis rates it 17.7 on the Intelligence Index, with 43.3 for coding. Against its lowest blended price of $0.31 per 1M, that is roughly 57 index points per dollar, which is the value ratio the leaderboards rank on. Median output speed is 142 tokens per second, with 2.32s to the first token.

The context window is 262,144 tokens at the reference host, but hosts report different limits, from 131,072 to 262,144, so the usable window depends on the provider you pick. It supports reasoning, tool use, and structured output. Accepted input modalities are Text, Image, Video, and Audio. The weights are open, so it can also be self-hosted or served through a gateway of your choice. Providers report the capability flags inconsistently, so verify a specific feature against the host you plan to use. Its training knowledge cuts off at 2025-04.

Specs & pricing

Input / output per 1M tokens
Official price·Alibaba
$0.40 / $3.20
Blended $1.10 · Cache read —
Lowest paid·AIHubMixGateway
$0.11 / $0.90
Blended $0.31 · 4.4× spread
1 more $0 channels
Context
262,144
Output limit
65,536
Knowledge cutoff
2025-04
Released / updated
2026-02-23 / 2026-02-23
Capabilities
✓ Reasoning✓ Tool use✓ Structured output✓ Temperature✓ Attachments
⚠ Providers report capability flags inconsistently
Modalities
TextImageVideoAudio
Weights
Hugging Face

Quality & performanceArtificial Analysis · Intelligence Index v4.3 · rep. tier Non-reasoning

Intelligence17.7
Value57
Coding43.3
Agentic—
Output speed142 tok/s
TTFT2.32 s
Value formulaIQ 17.7 ÷ min blended $0.310 = 57
Reasoning tier → intelligence / speed (higher tier = stronger but slower)
ReasoningIQ 15.6 · 129 tok/s
Non-reasoningIQ 17.7 · 142 tok/s

Quality is independently evaluated by Artificial Analysis. Speed/latency are model-level medians.

Available at 23 providers22 with public prices · 1 free

ProviderTierInputOutputCache readCache writeContextOutput limitStatus
NvidiaCloudFree——262,14465,536deprecated
AIHubMixGateway$0.11
tiered >128K: $0.28
$0.90——262,14465,536
EmpirioLabs AI
qwen3-5-122b-a10b
Gateway$0.12
tiered >128K: $0.29
$0.92$0.12—256,000 ⚠64,000
OrcaRouterGateway$0.12$0.92——262,14465,536
Merge GatewayGateway$0.12$0.92$0.023—131,072 ⚠32,768
SiliconFlow
Qwen/Qwen3.5-122B-A10B
Cloud$0.26$2.08——262,144262,144
Kilo GatewayGateway$0.26$2.08——262,14465,536
OpenRouterGateway$0.26$2.08——262,14465,536
Neon
qwen35-122b-a10b
Cloud$0.22$2.20——262,14425,000
OfoxGateway$0.29$2.29$0.29—256,000 ⚠64,000
AlibabaOfficialFirst-party$0.40$3.20——262,14465,536

Sorted by blended price (input×0.75 + output×0.25) asc. The official channel always shows regardless of rank. Whether a gateway's low price is actually usable can't be verified.

Reasoning control

Toggle (on / off)budget_tokenseffort = noneeffort = loweffort = mediumeffort = higheffort = maxeffort = minimaleffort = xhigh

Interleaved thinking (reasoning between tool calls) is declared by 2 of 23 providers.

2 / 23 providers expose no reasoning control (reasoning_options: []).

Your usage cost

1Merge Gateway$57.81
2AIHubMix$67.56
3EmpirioLabs AI$68.85
4OrcaRouter$68.85
5Neon$154.00
6SiliconFlow$156.00
16Alibaba · Official$240.00

1 more channels offer $0 (Nvidia); free tiers usually have rate limits and no SLA, excluded from ranking.

Switch to Merge Gateway to save $182.19/mo (76%)
Note: this is a gateway; verify availability and rate limits yourself.

Price historyone sample accumulated per data sync

Input listOutput listMin blended
$3.20$02026-08-052026-09-282026-08-05 · Input list $0.402026-08-06 · Input list $0.402026-08-07 · Input list $0.402026-08-08 · Input list $0.402026-08-09 · Input list $0.402026-08-10 · Input list $0.402026-08-11 · Input list $0.402026-08-12 · Input list $0.402026-08-13 · Input list $0.402026-09-28 · Input list $0.402026-08-05 · Output list $3.202026-08-06 · Output list $3.202026-08-07 · Output list $3.202026-08-08 · Output list $3.202026-08-09 · Output list $3.202026-08-10 · Output list $3.202026-08-11 · Output list $3.202026-08-12 · Output list $3.202026-08-13 · Output list $3.202026-09-28 · Output list $3.202026-08-05 · Min blended $0.322026-08-06 · Min blended $0.322026-08-07 · Min blended $0.322026-08-08 · Min blended $0.322026-08-09 · Min blended $0.322026-08-10 · Min blended $0.322026-08-11 · Min blended $0.322026-08-12 · Min blended $0.322026-08-13 · Min blended $0.322026-09-28 · Min blended $0.31

Benchmark1 items

NameConditionsScoreMetricSource
SWE-Bench Verified—72resolvedSource ↗

The same benchmark scores very differently across harness / dataset, so the qualifying conditions must be shown together.

Artificial Analysis evaluations8 items

GPQA Diamond82.7%
Humanity's Last Exam15.9%
Terminal-Bench Hard29.5%
Terminal-Bench 2.147.2%
τ²-Bench Telecom84.5%
τ³-Bench Banking10.3%
AA-LCR61.3%
IFBench50.8%

Individual evaluations run by Artificial Analysis, on the same reasoning tier as the intelligence score above. Each benchmark has its own task set and harness, so rows are not comparable with one another. The Intelligence Index above draws on a different, newer set of evaluations.

Related models

Qwen-Image-2.1same series$0 / $0Qwen 3.8 27B Hemingwaysame series$0.25 / $1.50Qwen 3.8 27B Cybersecuritysame series$0.10 / $0.60GLM-5.3-Flashcheaper alternative$0.15 / $0.50DeepSeek V4.1 Flashcheaper alternative$0.15 / $0.60GPT-5.6 Lunacheaper alternative$0.20 / $1.20GPT-5.4 nanocheaper alternative$0.20 / $1.25
Data partly from models.dev (MIT) · Alibaba official docs ↗