翻訳待ち：Quasar 438B: Intelligence, Performance and Price Analysis

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Artificial Analysis Multiverse Computing • Proprietary model • Released August 2026 Quasar 438B Intelligence, Performance & Price Analysis API Provider Benchmarks Model summary Intelligence 43 Artificial Analysis Intelligence Index 4 out of 4 units for Intelligence. Speed 182.7 Output tokens per second 4 out of 4 units for Speed. Cost In $0.60Out $1.80 $0.60 Cost per Intelligence Index task 4 out of 4 units for Cost. Verbosity 350M Output tokens from Intelligence Index 4 out of 4 units for Verbosity. Quasar 438B is amongst the leading models in intelligence, but particularly expensive when comparing to other models of similar price. It's also notably fast, however very verbose. The model supports text input, outputs text, and has a 1M tokens context window. Quasar 438B scores 43 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (median: 18). When evaluating the Intelligence Index, it generated 350M tokens, which is very verbose in comparison to the median of 67M. Pricing for Quasar 438B is $0.60 per 1M input tokens (expensive, median: $0.25) and $1.80 per 1M output tokens (expensive, median: $0.90). In total, it cost $1047.71 to evaluate Quasar 438B on the Intelligence Index. At 183 tokens per second, Quasar 438B is notably fast (106). ReasoningYes This page shows the reasoning version of this model. A non-reasoning variant may also exist. Input modality Supports: text Output modality Supports: text Context window1M ~1500 A4 pages of size 12 Arial font Metrics are compared against models of the same class: Non-reasoning models → compared only with other non-reasoning models Reasoning models → compared across both reasoning and non-reasoning Open weights models → compared only with other open weights models of the same size class: Tiny: ≤4B parameters Small: 4B–40B parameters Medium: 40B–150B parameters Large: >150B parameters Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio: $1 per 1M tokens Highlights Intelligence Artificial Analysis Intelligence Index · Higher is better Speed Output tokens per second · Higher is better Cost per Task Weighted average cost (USD) per Intelligence Index task · Lower is better Intelligence Artificial Analysis Intelligence Index Artificial Analysis Intelligence Index v4.1.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. 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 Artificial Analysis Intelligence Index v4.1.1 incorporates 9 evaluations: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them. 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. Benchmarks Intelligence Evaluations Intelligence evaluations measured independently by Artificial Analysis · Higher is better See more GDPval-AA v2 Agentic real-world work tasks, (Elo-500)/2000 𝜏³-Banking Agentic tool use Terminal-Bench v2.1 Agentic coding & terminal use SciCode Coding Humanity's Last Exam Reasoning & knowledge GPQA Diamond Scientific reasoning CritPt Physics reasoning AA-Omniscience Accuracy Knowledge AA-Omniscience Non-Hallucination Rate 1 - hallucination rate AA-LCR Long context reasoning AA-Briefcase Agentic knowledge work, Elo AutomationBench-AA Agentic SaaS workflows Harvey LAB-AA Legal agentic work, criterion pass rate EnterpriseOps-Gym-AA Agentic business operations AA-AnalystAgent Quantitative analysis on spreadsheets & documents IFBench Instruction following APEX-Agents-AA Long-horizon agentic tasks ITBench-AA Kubernetes incident root-cause analysis MMMU-Pro Visual reasoning While model intelligence generally translates across use cases, specific evaluations may be more relevant for certain use cases. Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them. 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. 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. Intelligence Index Comparisons 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. Artificial Analysis Intelligence Index v4.1.1 includes: GDPval-AA v2, 𝜏³-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GPQA Diamond, CritPt, AA-Omniscience, AA-LCR. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them. Token Use 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). Cost Cost per Intelligence Index Task Weighted average cost (USD) per Artificial Analysis Intelligence Index task, segmented by token type. Lower is better 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. 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. Context Window 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). Speed Measured by Output Speed (tokens per second) 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. Latency Measured by Time (seconds) to First 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. End-to-End Response Time 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). Frequently Asked Questions Common questions about Quasar 438B Quasar 438B was released on August 10, 2026. Quasar 438B was created by Multiverse Computing. Quasar 438B scores 43 on the Artificial Analysis Intelligence Index, placing it well above average among other reasoning models in a similar price tier (median: 18). Quasar 438B generates output at 182.7 tokens per second (based on Multiverse Computing's API), which is well above average compared to other reasoning models in a similar price tier (median: 105.9 t/s). Quasar 438B has a time to first token (TTFT) of 1.09s (based on Multiverse Computing's API), which is very competitive compared to other reasoning models in a similar price tier (median: 2.01s). Quasar 438B costs $0.60 per 1M input tokens (at the higher end, median: $0.25) and $1.80 per 1M output tokens (at the higher end, median: $0.90), based on Multiverse Computing's API. Quasar 438B costs $0.60 per 1M input tokens and $1.80 per 1M output tokens (based on Multiverse Computing's API). For a blended rate (7:2:1 cache hit/input/output ratio), this is $0.72 per 1M tokens. Pricing may vary by provider. Compare provider pricing When evaluated on the Intelligence Index, Quasar 438B generated 350M output tokens, which is at the higher end compared to other reasoning models in a similar price tier [truncated for AI cost control]