Multiverse Computing: Models Intelligence, Performance & Price
This analysis is intended to support you in choosing the best model provided by Multiverse Computing for your use-case.
Most Intelligent
Intelligence index
Total 4 models
Fastest
Output speed
Total 4 models
Lowest Price
Blended price (per 1M tokens)
Total 4 models
Multiverse Computing offers 4 models, each with different intelligence, performance, and pricing characteristics. Below is a comparison of the key metrics across models.
- For intelligence, the top models on Multiverse Computing are Qwen3.6 27B (38), Qwen3.6 27B (31), and HyperNova 60B 2605 (18).
- For output speed, the fastest models are HyperNova 60B 2605 (363 t/s), Qwen3.6 27B (225 t/s), and Qwen3.6 27B (173 t/s). Speed varies significantly across models, with a 382% difference between the fastest and slowest.
- For latency, Qwen3.6 27B (1.15s), Mistral Small 3.1 (1.52s), and HyperNova 60B 2605 (6.41s) offer the lowest time to first answer token.
- For pricing, HyperNova 60B 2605 ($0.05), Mistral Small 3.1 ($0.12), and Qwen3.6 27B ($0.23) offer the lowest blended prices per 1M tokens. Prices vary up to 4.5x across models.
- For context window size, Qwen3.6 27B (262k), Qwen3.6 27B (262k), and HyperNova 60B 2605 (131k) support the largest context windows on Multiverse Computing.
- HyperNova 60B 2605 offers both the fastest output and best pricing, making it attractive for throughput-sensitive and cost-conscious applications. Qwen3.6 27B leads in intelligence for tasks that require the highest quality.
Highlights
Intelligence Evaluations
Artificial Analysis Intelligence Index
Intelligence Evaluations
Agentic real-world work tasks, (Elo-500)/2000
Agentic tool use
Agentic coding & terminal use
Coding
Reasoning & knowledge
Scientific reasoning
Physics reasoning
Knowledge
1 - hallucination rate
Long context reasoning
Agentic knowledge work, Elo
Agentic SaaS workflows
Legal agentic work, criterion pass rate
Agentic business operations
Quantitative analysis on spreadsheets & documents
Instruction following
Long-horizon agentic tasks
Kubernetes incident root-cause analysis
Visual reasoning
Intelligence Index vs. Price
Context Window
Context Window
Pricing
Intelligence Index vs. Price
Performance Summary
Output Speed vs. Price
Speed
Measured by Output Speed (tokens per second)
Output Speed
Latency
Measured by Time (seconds) to First Token
Latency: Time To First Answer Token
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 vs. Price
Key definitions
Frequently Asked Questions
Common questions about Multiverse Computing
Multiverse Computing offers 4 models that we track: Qwen3.6 27B, Qwen3.6 27B, HyperNova 60B 2605, and Mistral Small 3.1.
The most intelligent model available on Multiverse Computing is Qwen3.6 27B with an Intelligence Index score of 38.
The fastest model on Multiverse Computing by output speed is HyperNova 60B 2605 at 362.5 tokens per second.
The model with the lowest time to first answer token on Multiverse Computing is Qwen3.6 27B at 1.15s. Lower latency means faster initial response time.
The most affordable model on Multiverse Computing by blended price is HyperNova 60B 2605 at $0.05 per 1M tokens (7:2:1 cache hit/input/output ratio).
Prices on Multiverse Computing vary up to 5x across models, from $0.05 per 1M tokens for HyperNova 60B 2605 to $0.23 per 1M tokens for Qwen3.6 27B.
Yes, Multiverse Computing offers an OpenAI-compatible API, making it easy to switch from OpenAI or use existing OpenAI SDK integrations.
Yes, all 4 models on Multiverse Computing support JSON mode for structured output.
3 of 4 models on Multiverse Computing support function calling (tool use).
Yes, Multiverse Computing offers 2 reasoning models: Qwen3.6 27B and HyperNova 60B 2605. Reasoning models use extended thinking to work through complex problems before providing an answer.
Yes, all 4 models on Multiverse Computing are open weight models.
Yes, provider performance can vary over time due to infrastructure changes, load balancing, and updates. We continuously benchmark all providers and display historical performance trends in the "Over Time" charts.
When choosing a model on Multiverse Computing, consider: intelligence (for quality-sensitive tasks), output speed (for throughput-intensive tasks), latency (for interactive applications requiring quick first responses), pricing (for cost-sensitive workloads), and features like context window size, JSON mode, or function calling support.