Model profile
qwen

Qwen: Qwen3 Next 80B A3B Instruct

Qwen: Qwen3 Next 80B A3B Instruct is a budget text-first model from qwen with a fast runtime profile, extended context posture, and the clearest fit around long-context research / multimodal.

Best for: Long-context research / MultimodalFast latencyExtended contextBudget pricing
Intelligence
20.1

Benchmark blend

Coding
15.3

Dev workflow signal

Context
131K Tokens

Extended

Input Price
$0.50

Budget tier

Decision snapshot
52

Qwen: Qwen3 Next 80B A3B Instruct currently reads as a budget text-first option with extended context and a fast runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Multimodal
Latency tier
Fast
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
20.1
20

General reasoning and benchmark headroom.

Limited
Speed
158 tok/s
91

TTFT 0.98s

Above average
Context
131K Tokens
76

How much prompt and task state can stay in view.

Competitive
Price
$0.50
86

$2.00 output / 1M

Efficient

Editorial Profile

Qwen: Qwen3 Next 80B A3B Instruct in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Use-case specificCoding score 15Math score 66

Qwen3-Next-80B-A3B-Instruct is an instruction-tuned chat model in the Qwen3-Next series optimized for fast, stable responses without “thinking” traces. It targets complex tasks across reasoning, code generation, knowledge QA, and multilingual use, while remaining robust on alignment and formatting. Compared with prior Qwen3 instruct variants, it focuses on higher throughput and stability on ultra-long inputs and multi-turn dialogues, making it well-suited for RAG, tool use, and agentic workflows that require consistent final answers rather than visible chain-of-thought. The model employs scaling-efficient training and decoding to improve parameter efficiency and inference speed, and has been validated on a broad set of public benchmarks where it reaches or approaches larger Qwen3 systems in several categories while outperforming earlier mid-sized baselines. It is best used as a general assistant, code helper, and long-context task solver in production settings where deterministic, instruction-following outputs are preferred.

Identity

qwen text-first profile

Positioning

Long-context research / Multimodal with extended context and fast runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • Large context headroom supports repo-wide prompts and long research sessions.

  • Latency and throughput look responsive enough for interactive loops.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

Best fit
  • Long-context summarization, repo analysis, and policy or document review.

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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
20.1
MMLU Pro
81.9%
GPQA
73.8%
HLE
7.3%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
15.3
LiveCodeBench
0.684
SciCode
30.7%
Math

Formal reasoning, structured problem solving, and competition-style math.

Math Index
66.3
AIME 2025
66.3%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
39.7%
TAU2
21.6%
TerminalBench Hard
7.6%
LCR
51.3%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
131K Tokens
Vision
Text-first
Modalities
text->text, text
Tokenizer
Qwen3
Moderation
No
Supported Parameters
frequency_penaltylogit_biasmax_tokensmin_ppresence_penaltyrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.50
Output
per 1M output tokens
$2.00
Blended
AA 3:1 mix
$0.88

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.