Crusoe: Models Intelligence, Performance & Price

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

Crusoe

This analysis is intended to support you in choosing the best model provided by Crusoe for your use-case.

Most Intelligent

Updated

GLM-5.3 (max) (FP4)

GLM-5.3-Flash (FP4)

Qwen3.8 27B (xhigh)

Nemotron 3.5 Super VL

Qwen3.8 27B (non-reasoning)

Intelligence Index

Total 15 models

Lowest Time per Task

Qwen3.8 27B (non-reasoning)

3.0 min

Nemotron 3.5 Lightning (BF16)

4.5 min

gpt-oss-120b (high)

5.5 min

Qwen3.8 27B (xhigh)

6.7 min

GLM-5.3 (max) (FP4)

11.7 min

Time per Intelligence Index task (minutes)

Total 15 models

Lowest Price

Nemotron 3.5 Lightning (BF16)

$0.05

Nemotron 3 Nano (non-reasoning) (FP8)

$0.05

Nemotron 3 Nano (FP8)

$0.05

gpt-oss-120b (high)

$0.07

gpt-oss-120b (low)

$0.07

Blended price (per 1M tokens)

Total 15 models

Crusoe offers 15 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 Crusoe are GLM-5.3 (max) (FP4) (32), GLM-5.3-Flash (FP4) (28), and Qwen3.8 27B (xhigh) (20).
For time per task, Qwen3.8 27B (non-reasoning) (3.0 min), Nemotron 3.5 Lightning (BF16) (4.5 min), and gpt-oss-120b (high) (5.5 min) finish an Intelligence Index task fastest on Crusoe. Time per task varies significantly across models, with the slowest taking 3.9x longer than the quickest.
For latency, DeepSeek V4.1 Flash (non-reasoning) (0.75s), Nemotron 3 Nano (non-reasoning) (FP8) (0.86s), and gpt-oss-120b (low) (1.46s) offer the lowest time to first answer token.
For pricing, Nemotron 3.5 Lightning (BF16) ($0.05), Nemotron 3 Nano (non-reasoning) (FP8) ($0.05), and Nemotron 3 Nano (FP8) ($0.05) offer the lowest blended prices per 1M tokens.
For context window size, GLM-5.3-Flash (FP4) (1M), DeepSeek V4.1 Flash (non-reasoning) (1M), and DeepSeek V4.1 Flash (max) (1M) support the largest context windows on Crusoe.

Updated

Intelligence

Artificial Analysis Intelligence Index · Higher is better

Time per Task

Inference time (minutes) per Intelligence Index task · Lower is better

Price

USD per 1M tokens (blended) · Lower is better

IntelligenceUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index v5.0.1 incorporates 9 evaluations: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), and Terminal-Bench-Science 0.1

Artificial Analysis Intelligence Index v5.0.1 includes: AA-Briefcase v1.1, AutomationBench-AA, Terminal-Bench 4.0, FrontierCode-AA 2.0, AA-Omniscience v1.1, GDP.pdf, HLE-Diamond, CritPt v2 (Hard Subset), Terminal-Bench-Science 0.1. See Intelligence Index methodology for further details, including a breakdown of each evaluation and how we run them.

Evaluations

Evaluations measured independently by Artificial Analysis · AA-PersonalityBench scores are percentile ranks from 1 to 100

AA-Briefcase v1.1

Agentic knowledge work, Elo

AA-Omniscience v1.1 AccuracyUpdated

Knowledge

AA-Omniscience v1.1 Non-Hallucination RateUpdated

1 - hallucination rate

AutomationBench-AA

Agentic SaaS workflows

Terminal-Bench-Science 0.1New

Agentic scientific research workflows in a terminal

FrontierCode-AA 2.0New

Agentic software engineering in open-source repositories

HLE-DiamondNew

Reasoning & knowledge

GDP.pdf

Professional document reasoning, All-pass

Terminal-Bench 4.0

Agentic coding & terminal use

CritPt v2 (Hard Subset)

Physics reasoning

AA-AnalystAgent

Quantitative analysis on spreadsheets & documents

Harvey LAB-AA v1.1Updated

Legal agentic work, Hallucination-Gated All-Pass Rate

EnterpriseOps-Gym-AA

Agentic business operations

GDPval-AA v2.1

Agentic real-world work tasks, Elo

AA-3DNew

Agentic interactive 3D experiences, Elo

Code2Video-AANew

Product and brand videos designed in code, Elo

ITBench-AA

Kubernetes incident root-cause analysis

MLCR-AAUpdated

Medical long context reasoning

MMMU-Pro

Visual reasoning

AA-PersonalityBench: Expressiveness ScoreNew

Rich, varied, well-crafted language

AA-PersonalityBench: Organization ScoreNew

Well-structured, scannable, answer-first

AA-PersonalityBench: Transparency ScoreNew

Truthful, acknowledges uncertainty, non-sycophantic

AA-PersonalityBench: Slop ScoreNew

Use of filler and generic language

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

Intelligence Index vs. Price

Blended at 7:2:1 (cache-input-output) · USD per 1M tokens (blended)

Most attractive quadrant

Pareto line

While higher intelligence models are typically more expensive, they do not all follow the same price-quality curve.

Pricing

Cost per Intelligence Index Task

Weighted average cost (USD) per 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.

Intelligence Index vs. Price

Blended at 7:2:1 (cache-input-output) · USD per 1M tokens (blended)

Most attractive quadrant

Pareto line

While higher intelligence models are typically more expensive, they do not all follow the same price-quality curve.

Speed

Average of the single-request and multi-turn agentic workloads measured

Time per Intelligence Index Task

Output tokens divided by end-to-end output speed (minutes per Intelligence Index task) · Lower is better

The weighted average time (minutes) per Artificial Analysis Intelligence Index task: output tokens per task divided by output speed, weighted by each benchmark's weight in the Intelligence Index.

Speed and latency metrics average each endpoint's runs over the past 5 days, and the error rate pools them. We run the benchmark every 6 hours, and endpoints of deprecated models every 18 hours.

Quality

Endpoint Accuracy Index

Each endpoint's accuracy as a % of its model's reference endpoint · Color shows whether the gap is statistically significant

Composite measure of how much of a model's accuracy a given provider endpoint preserves, from re-running BFCL v4-500, HLE-250 and AA-LCR-25 against that endpoint. Where a self-hosted reference endpoint exists, scores are expressed as a percentage of that reference (100 = matches reference); lower scores indicate accuracy lost to quantisation, sampling defaults, or other endpoint-side configuration. Scores are point-in-time snapshots. Methodology.

Latency

Average of the single-request and multi-turn agentic workloads measured

Time to First Answer Token

Seconds until the first answer token after any reasoning · Average of the single-request and multi-turn agentic workloads measured · Lower is better

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.

Speed and latency metrics average each endpoint's runs over the past 5 days, and the error rate pools them. We run the benchmark every 6 hours, and endpoints of deprecated models every 18 hours.

Reliability

Share of requests the endpoint failed, across all the single-request and multi-turn agentic workloads measured

Endpoint Error Rate

Share of requests the endpoint failed with an error, a rate limit or a timeout · Across all the single-request and multi-turn agentic workloads measured · Lower is better

Share of requests over the past 5 days that the endpoint failed with an error, a rate limit or a timeout. Failures on our side are left out of both counts. Across several workloads, the failures and requests are pooled before dividing.

Speed and latency metrics average each endpoint's runs over the past 5 days, and the error rate pools them. We run the benchmark every 6 hours, and endpoints of deprecated models every 18 hours.

Caching

Cache Hit Rate

Median across multi-turn agentic session runs in the last four weeks, updated Oct 26, 2026 · Higher is better

The share of cacheable input tokens served from the provider's prompt cache, measured during multi-turn agentic sessions. We regularly measure each endpoint and report a representative rate based on recent measurements. Higher values mean more of the input is billed at the cheaper cache-hit price, where the provider offers one, which lowers the effective cost of agentic workloads. For further details, see our methodology page.

Cost per Task vs. Cache Hit Rate

Weighted average cost (USD) per Intelligence Index task · Cache hit rate: median of the last four weeks, updated Oct 26, 2026

Most attractive quadrant

Pareto line


								Further Analysis
GLM-5.3 (max) (FP4)	32	$5.10	11.5	27.70	0.0%	786k	Open	Model Provider
GLM-5.3-Flash (FP4)	28	$0.94	12.2	25.22	0.0%	1M	Open	Model Provider
Qwen3.8 27B (xhigh)	20	$2.61	6.7	22.18	0.0%	262k	Open	Model Provider
Nemotron 3.5 Super VL	14	$0.52	32.8	41.23	36.0%	262k	Open	Model Provider
Qwen3.8 27B (non-reasoning)	10	$3.31	3.1	1.60	0.0%	262k	Open	Model Provider
Nemotron 3.5 Lightning (BF16)	6	$0.26	4.5	16.34	0.0%	262k	Open	Model Provider
Gemma 4 31B	4	--	--	29.50	3.4%	262k	Open	Model Provider
gpt-oss-120b (high)	4	$0.12	5.6	8.35	0.0%	131k	Open	Model Provider
Nemotron 3 Nano (non-reasoning) (FP8)	--	--	--	0.86	8.7%	262k	Open	Model Provider
Nemotron 3 Super (FP8)	--	--	--	42.31	9.4%	262k	Open	Model Provider
DeepSeek V4.1 Flash (non-reasoning)	--	--	--	0.75	0.0%	1M	Open	Model Provider
Nemotron 3 Nano (FP8)	--	--	--	15.18	9.3%	262k	Open	Model Provider
gpt-oss-120b (low)	--	--	--	1.45	0.9%	131k	Open	Model Provider
Gemma 4 31B (non-reasoning)	--	--	--	4.45	3.4%	262k	Open	Model Provider
DeepSeek V4.1 Flash (max)	--	--	--	35.94	0.0%	1M	Open	Model Provider

Key definitions

Average cost per task in the index. Costs are split by input, cache hit, cache write, reasoning, and answer token pricing where canonical token counts are available.

Frequently Asked Questions

Common questions about Crusoe

Crusoe offers 15 models that we track: GLM-5.3 (max) (FP4), GLM-5.3-Flash (FP4), Qwen3.8 27B (xhigh), Nemotron 3.5 Super VL, Qwen3.8 27B (non-reasoning), Nemotron 3.5 Lightning (BF16), Gemma 4 31B, gpt-oss-120b (high), Nemotron 3 Nano (non-reasoning) (FP8), Nemotron 3 Super (FP8), DeepSeek V4.1 Flash (non-reasoning), Nemotron 3 Nano (FP8), gpt-oss-120b (low), Gemma 4 31B (non-reasoning), and DeepSeek V4.1 Flash (max).

The most intelligent model available on Crusoe is GLM-5.3 (max) (FP4) with an Intelligence Index score of 32.

The model with the lowest time per task on Crusoe is Qwen3.8 27B (non-reasoning) at 3.0 minutes per Intelligence Index task. Time per task is the output tokens per task divided by end-to-end output speed, so lower means a task finishes sooner.

The model with the lowest time to first answer token on Crusoe is DeepSeek V4.1 Flash (non-reasoning) at 0.75s. Lower latency means faster initial response time.

The most affordable model on Crusoe by blended price is Nemotron 3.5 Lightning (BF16) at $0.051 per 1M tokens (7:2:1 cache hit/input/output ratio).

Prices on Crusoe vary up to 17.6x across models, from $0.051 per 1M tokens for Nemotron 3.5 Lightning (BF16) to $0.90 per 1M tokens for GLM-5.3 (max) (FP4).

Yes, Crusoe offers an OpenAI-compatible API, making it easy to switch from OpenAI or use existing OpenAI SDK integrations.

Yes, all 15 models on Crusoe support JSON mode for structured output.

14 of 15 models on Crusoe support function calling (tool use).

Yes, Crusoe offers 11 reasoning models: GLM-5.3 (max) (FP4), GLM-5.3-Flash (FP4), Qwen3.8 27B (xhigh), Nemotron 3.5 Super VL, Nemotron 3.5 Lightning (BF16), Gemma 4 31B, gpt-oss-120b (high), Nemotron 3 Super (FP8), Nemotron 3 Nano (FP8), gpt-oss-120b (low), and DeepSeek V4.1 Flash (max). Reasoning models use extended thinking to work through complex problems before providing an answer.

Yes, all 15 models on Crusoe 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 Crusoe, consider: intelligence (for quality-sensitive tasks), time per task (how quickly the model completes an Intelligence Index task), 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.

Crusoe: Models Intelligence, Performance & Price

Most Intelligent

Lowest Time per Task

Lowest Price

Intelligence

Time per Task

Price

IntelligenceUpdated

Artificial Analysis Intelligence Index

Artificial Analysis Intelligence Index

Evaluations

Intelligence Evaluation Relevance

Artificial Analysis Intelligence Index

Intelligence Index vs. Price

Price-Quality Variance

Pricing

Cost per Intelligence Index Task

Cost per Intelligence Index Task

Intelligence Index vs. Price

Price-Quality Variance

Speed

Time per Intelligence Index Task

Time per Intelligence Index Task

Time Period

Quality

Endpoint Accuracy Index

Endpoint Accuracy Index

Latency

Time to First Answer Token

Time to First Answer Token

Time Period

Reliability

Endpoint Error Rate

Endpoint Error Rate

Time Period

Caching

Cache Hit Rate

What Cache Hit Rate Shows

Cost per Task vs. Cache Hit Rate

Cost per Intelligence Index Task

What Cache Hit Rate Shows

Key definitions

Index Cost per Task

Frequently Asked Questions

What models does Crusoe offer?

What is the most intelligent model on Crusoe?

Which model has the lowest time per task on Crusoe?

Which model has the lowest latency on Crusoe?

What is the cheapest model on Crusoe?

How much do prices vary across models on Crusoe?

Is Crusoe OpenAI compatible?

Does Crusoe support JSON mode?

Does Crusoe support function calling?

Does Crusoe offer reasoning models?

Does Crusoe offer open weight models?

Does Crusoe's performance change over time?

How do I choose a model on Crusoe?