Qwen3.8-Flash-Next vs. Grok 4.6 (high)

Name: Intelligenz
Creator: Artificial Analysis
License: https://artificialanalysis.ai/docs/legal/Terms-of-Use.pdf

Vergleich von Qwen3.8-Flash-Next und Grok 4.6 (high) nach Intelligenz, Preis, Geschwindigkeit, Kontextfenster und weiteren Merkmalen.

Einzelheiten zu unserer Methodik finden Sie auf unserer Methodikseite.

	Alibaba	SpaceXAI
Intelligenz
Intelligence Index	40	44
AA-Briefcase	1587	1534
GDPval-AA v2	1647	1643
AutomationBench-AA	56%	67%
Terminal-Bench 4.0	25%	21%
SciCode	51%	56%
Humanity's Last Exam	38%	43%
GDP.pdf	16%	17%
CritPt	11%	17%
AA-Omniscience	−10	30
AA-LCR v1.1	80%	80%
Kosten
Preis pro 1 Mio. Tokens	$0.0882	$1.35
Eingabepreis pro 1 Mio. Tokens	$0.15	$2.00
Ausgabepreis pro 1 Mio. Tokens	$0.47	$6.00
Cache-Hit-Preis pro 1 Mio. Tokens	$0.016	$0.50
Kosten pro Aufgabe	$0.37	$1.86
Kosten für den Intelligence Index	363 $	2.352 $
Tokenverbrauch
Ausgabe-Tokens pro Aufgabe	108k	36k
Reasoning-Tokens pro Aufgabe	72k	19k
Ausgabe-Tokens für den Intelligence Index	245M	94M
Leistung
Ausgabegeschwindigkeit	53 Tokens/s	64 Tokens/s
Zeit bis zum ersten Token	2.69 s	40.56 s
Zeit bis zum ersten Antwort-Token	40.57 s	40.56 s
Ende-zu-Ende-Antwortzeit	50.04 s	48.32 s
Zeit pro Aufgabe	1543.91 s	548.28 s
Technische Spezifikationen
Kontextfenster	256k Tokens~384 A4-Seiten in Arial mit Schriftgröße 12	500k Tokens~750 A4-Seiten in Arial mit Schriftgröße 12
Veröffentlichungsdatum	August 2026	August 2026
Gesamtparameter	180B
Aktive Parameter	6B
Reasoning	Ja	Ja
Eingabemodalität	Unterstützt: Text, Bilder und Video	Unterstützt: Text und Bilder
Ausgabemodalität	Unterstützt: Text	Unterstützt: Text
Offene Gewichte	Ja	Nein
Lizenz	Qwen Community License 1.0
Lizenz erlaubt uneingeschränkte kommerzielle Nutzung	Ja

Wichtigste Ergebnisse

Updated

Intelligenz

Artificial Analysis Intelligence Index · Higher is better

Geschwindigkeit

Output tokens per second · Higher is better

Kosten pro Aufgabe

Weighted average cost (USD) per Intelligence Index task · Lower is better

IntelligenzUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v4.3 incorporates 10 evaluations: AA-Briefcase, GDPval-AA v2, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1

Artificial Analysis Intelligence Index v4.3 includes: AA-Briefcase, GDPval-AA v2, AutomationBench-AA, Terminal-Bench 4.0, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, AA-LCR v1.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Indicates whether the model weights are available. Models are labelled as 'Commercial Use Restricted' if commercial use is limited by conditions, and as 'Non-commercial' if the license prohibits commercial use.

FähigkeitsindizesUpdated

Misst die Leistung von Modellen bei bestimmten Fähigkeiten und in bestimmten Branchen

Artificial Analysis Finance & Accounting Index

Incorporates 7 evaluations: AA-Omniscience, GDPval-AA v2, AA-Briefcase, Humanity's Last Exam, AutomationBench-AA, AA-LCR v1.1, GDP.pdf · Higher is better

Benchmarks

Intelligence Evaluations

Intelligence evaluations measured independently by Artificial Analysis · Higher is better

AA-Briefcase

Agentic knowledge work, (Elo-500)/2000

GDPval-AA v2

Agentic real-world work tasks, (Elo-500)/2000

AutomationBench-AAUpdated

Agentic SaaS workflows

Terminal-Bench 4.0New

Agentic coding & terminal use

SciCode

Coding

Humanity's Last Exam

Reasoning & knowledge

GDP.pdfNew

Professional document reasoning, All-pass

CritPt

Physics reasoning

AA-Omniscience Accuracy

Knowledge

AA-Omniscience Non-Hallucination Rate

1 - hallucination rate

AA-LCR v1.1

Long context reasoning

Harvey LAB-AA

Legal agentic work, criterion pass rate

EnterpriseOps-Gym-AA

Agentic business operations

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

𝜏³-Banking

Agentic tool use

ITBench-AA

Kubernetes incident root-cause analysis

MMMU-Pro

Visual reasoning

MLCR-AANew

Medical long context reasoning

While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases.

AA-Briefcase

AA-Briefcase Elo

AA-Briefcase is an agentic knowledge work benchmark developed by Artificial Analysis. AA-Briefcase Elo is a combined metric that aggregates rubric pass rate, analytical quality Elo and presentation Elo · Higher is better

AA-Briefcase Elo is a combined metric that aggregates analytical quality Elo, presentation Elo, and rubric pass rate, with rubric performance converted into Elo via synthetic head-to-head matches. Elo and 95% confidence interval bounds are clamped at 0.

AA-Omniscience

AA-Omniscience Index

AA-Omniscience Index (higher is better) measures knowledge reliability and hallucination. It rewards correct answers, penalizes hallucinations, and has no penalty for refusing to answer. Scores range from -100 to 100, where 0 means as many correct as incorrect answers, and negative scores mean more incorrect than correct.

Openness Index

Artificial Analysis Openness Index: Score

Openness Index assesses model openness on a 0 to 100 normalized scale (higher is more open)

Vergleiche des Intelligence Index

Intelligence Index vs. Cost per Intelligence Index Task

Artificial Analysis Intelligence Index · Weighted average cost (USD) per Artificial Analysis Intelligence Index task

Most attractive quadrant

Pareto line

Weighted average cost per Intelligence Index task. Each evaluation’s cost is calculated from input, cache hit, cache write, reasoning, and answer token prices, divided by task count, and weighted by its Intelligence Index weight.

Tokenverbrauch

Output Tokens per Intelligence Index Task

Weighted average number of output tokens used to run one task in the Artificial Analysis Intelligence Index

The number of tokens required per Intelligence Index task. This is calculated by multiplying the output tokens per eval by the relative weights of each benchmark in the Intelligence Index, then dividing by task count (excluding repeats).

Kosten

Cost per Intelligence Index Task

Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better

Cost to Run Artificial Analysis Intelligence Index

Cost (USD) to run all evaluations in the Artificial Analysis Intelligence Index

The cost to run the evaluations in the Artificial Analysis Intelligence Index, calculated using the model's input, cache hit, cache write, reasoning, and answer token prices and the number of tokens used across evaluations (excluding repeats).

Pricing: Cache Hit, Input, and Output

Price (USD per M Tokens)

Price per token for cached prompts (previously processed), typically offering a significant discount compared to regular input price, represented as USD per million tokens. The values shown here are the cache hit price; cache write and cache storage are billed separately and vary by provider — see "Cache pricing by provider" for detail.

Kontextfenster

Context Window

Context window: tokens limit · Higher is better

Larger context windows are relevant to RAG (Retrieval Augmented Generation) LLM workflows which typically involve reasoning and information retrieval of large amounts of data.

Maximum number of combined input & output tokens. Output tokens commonly have a significantly lower limit (varied by model).

Geschwindigkeit

Gemessen anhand der Ausgabegeschwindigkeit (Tokens pro Sekunde)

Output Speed

Output tokens per second · Higher is better

Tokens per second received while the model is generating tokens (ie. after first chunk has been received from the API for models which support streaming).

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Time per Intelligence Index Task

Weighted average decode time (minutes) per task; excludes TTFT and overhead time · Lower is better

The weighted average time (seconds) per Artificial Analysis Intelligence Index task. This is calculated by dividing output tokens per task by output speed, weighted by the relative weights of each benchmark in the Intelligence Index.

Latenz

Gemessen anhand der Zeit (in Sekunden) bis zum ersten Token

Latency: Time To First Answer Token

Seconds to first answer token received · Accounts for reasoning model 'thinking' time

Time to first answer token received, in seconds, after API request sent. For reasoning models, this includes the 'thinking' time of the model before providing an answer. For models which do not support streaming, this represents time to receive the completion.

Ende-zu-Ende-Antwortzeit

Seconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed

End-to-End Response Time

Seconds to output 500 tokens, including reasoning model 'thinking' time · Lower is better

Seconds to receive a 500 token response. Key components:

Input time: Time to receive the first response token
Thinking time (only for reasoning models): Time reasoning models spend outputting tokens to reason prior to providing an answer. Amount of tokens based on the average reasoning tokens across a diverse set of 60 prompts (methodology details).
Answer time: Time to generate 500 output tokens, based on output speed

Figures represent performance of the model's first-party API (e.g. OpenAI for o1) or the median across providers where a first-party API is not available (e.g. Meta's Llama models).

Modellgröße (nur Modelle mit offenen Gewichten)

Model Size: Total and Active Parameters

Comparison between total model parameters and parameters active during inference

The total number of trainable weights and biases in the model, expressed in billions. These parameters are learned during training and determine the model's ability to process and generate responses.

The number of parameters actually executed during each inference forward pass, expressed in billions. For Mixture of Experts (MoE) models, a routing mechanism selects a subset of experts per token, resulting in fewer active than total parameters. Dense models use all parameters, so active equals total.

Häufig gestellte Fragen

Grok 4.6 (high) ist intelligenter. Grok 4.6 (high) erzielt im Artificial Analysis Intelligence Index einen Wert von 44, verglichen mit Qwen3.8-Flash-Next mit 40.

Grok 4.6 (high) ist schneller. Grok 4.6 (high) erzeugt 64.5 Tokens pro Sekunde, verglichen mit Qwen3.8-Flash-Next mit 52.8 Tokens pro Sekunde.

Qwen3.8-Flash-Next ist günstiger. Qwen3.8-Flash-Next kostet $0.09 pro 1 Mio. Tokens, verglichen mit Grok 4.6 (high) mit $1.35 pro 1 Mio. Tokens bei einem Verhältnis von 7:2:1 für Cache-Treffer, Eingabe und Ausgabe.

Qwen3.8-Flash-Next hat die niedrigere Latenz. Die Zeit bis zum ersten Token beträgt bei Qwen3.8-Flash-Next 2.69 s, verglichen mit Grok 4.6 (high) mit 40.56 s.

Grok 4.6 (high) hat das größere Kontextfenster. Grok 4.6 (high) unterstützt 500k Tokens, verglichen mit Qwen3.8-Flash-Next mit 260k Tokens.

Qwen3.8-Flash-Next vs. Grok 4.6 (high)

Intelligenz

Geschwindigkeit

Kosten pro Aufgabe

IntelligenzUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index by Open Weights / Proprietary

Artificial Analysis Intelligence Index

Open Weights

FähigkeitsindizesUpdated

Artificial Analysis Finance & Accounting Index

Benchmarks

Intelligence Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

AA-Briefcase

AA-Briefcase Elo

AA-Briefcase Elo

AA-Omniscience

AA-Omniscience Index

AA-Omniscience Index

Openness Index

Artificial Analysis Openness Index: Score

Vergleiche des Intelligence Index

Intelligence Index vs. Cost per Intelligence Index Task

Cost per Intelligence Index Task

Artificial Analysis Intelligence Index

Tokenverbrauch

Output Tokens per Intelligence Index Task

Output Tokens per Intelligence Index Task

Kosten

Cost per Intelligence Index Task

Cost per Intelligence Index Task

Cost to Run Artificial Analysis Intelligence Index

Cost to Run Artificial Analysis Intelligence Index

Pricing: Cache Hit, Input, and Output

Cache Hit

Kontextfenster

Context Window

Context Window for RAG

Context Window

Geschwindigkeit

Output Speed

Output Speed

Model Performance Representation

Time per Intelligence Index Task

Time per Intelligence Index Task

Latenz

Latency: Time To First Answer Token

Time to First Answer Token

Ende-zu-Ende-Antwortzeit

End-to-End Response Time

End-to-End Response Time

Model Performance Representation

Modellgröße (nur Modelle mit offenen Gewichten)

Model Size: Total and Active Parameters

Total Parameters

Active Parameters at Inference Time

Häufig gestellte Fragen

Welches Modell ist intelligenter, Qwen3.8-Flash-Next oder Grok 4.6 (high)?

Welches Modell ist schneller, Qwen3.8-Flash-Next oder Grok 4.6 (high)?

Welches Modell ist günstiger, Qwen3.8-Flash-Next oder Grok 4.6 (high)?

Welches Modell hat die niedrigere Latenz, Qwen3.8-Flash-Next oder Grok 4.6 (high)?

Welches Modell hat das größere Kontextfenster, Qwen3.8-Flash-Next oder Grok 4.6 (high)?