Head-to-Head Model Comparison

Laguna XS 2.1 vs GPT-6 Astra

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Vérifié par l’équipe de recherche de GetInfoUsDernière révision : October 2026Politique éditoriale
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PoolsideOpenAIlightweightflagship

Compare Laguna XS 2.1 by Poolside against GPT-6 Astra by OpenAI across real-world coding, reasoning, token economics, and latency.

Comparaison Face-à-Face (VS)

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Live comparison: no in-depth editorial review has been published for this pair yet.

Popular Matchups:
Poolside

Laguna XS 2.1

Codage ultra-économique

de Poolside hyper-efficient codage modèle développé pour instant linting, unit test synthèse, et élevé-frequency développeur flux de travail.

Intelligence Index77 / 100
Input Token:

$0.05 / 1M

Output Token:

$0.20 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

N/A

OpenAI

GPT-6 Astra

Agent d'OS autonome

d'OpenAI late-2026 phare modèle de fondation intégrant natif utilisation de l'ordinateur agents, dynamic UI orchestration, et profond STEM raisonnement.

Intelligence Index96 / 100
Input Token:

$10.00 / 1M

Output Token:

$50.00 / 1M

Context Window:

272K tokens

SWE-bench Verified:

82.4%

Différences Clés en un Coup d’Œil

  • Laguna XS 2.1 is 234.3x cheaper for API usage (input + output tokens combined).
  • GPT-6 Astra has a larger context window (272K vs 262K tokens).
  • Laguna XS 2.1 is faster at 165 tok/s vs 100 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
CaractéristiqueLaguna XS 2.1GPT-6 Astra
Intelligence Index
Composite reasoning & knowledge score (0-100)
7796
Provider
PoolsideOpenAI
Input Price
Cost per million input tokens
$0.05 / 1M$10.00 / 1M
Output Price
Cost per million output tokens
$0.20 / 1M$50.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.03 / 1M$7.70 / 1M
Context Window
Maximum input + output tokens
262.1K tokens~393 A4 pages of text272K tokens~408 A4 pages of text
Max Output Tokens
Per single request
8K128K
Throughput Speed
Tokens generated per second
165 tok/s100 tok/s
SWE-bench Verified
Real-world software engineering
N/A82.4%
GPQA Diamond
Graduate-level reasoning
N/A80.9%
Cached Input Price
Prompt caching discount per 1M tokens
$0.01 / 1M$1.00 / 1M

Qui gagne où ?

At-a-glance decision framework
Code

Evaluations ongoing

Coût
Laguna XS 2.1
234.3x cheaper monthly runtime
Vitesse
Laguna XS 2.1
+65% faster generation
Contexte
GPT-6 Astra
272K tokens window

Scores de Benchmarks Vérifiés

Official evaluation metrics verified by independent third-party research labs.

Laguna XS 2.1GPT-6 Astra
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

N/Avs82.4%
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

N/Avs80.9%
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

74%vs89.9%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1280ptsvs1520pts
Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs18.2%
Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

N/Avs63.8%
SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs54.9%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

64%vs79.8%
AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

N/Avs93.6%
Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

N/Avs91.2%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

165 tok/s vs100 tok/s

Latence et Temps de Réponse

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
Laguna XS 2.10.24s
GPT-6 Astra0.95s
Est. End-to-End Response (500 tokens)TTFT + generation time
Laguna XS 2.13.27s
GPT-6 Astra5.95s

Simulateur Interactif de Coût de Tokens

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Laguna XS 2.1 Monthly API Bill
$0.26 / month
$0.05/1M in · $0.2/1M out
GPT-6 Astra Monthly API Bill
$61.50 / month
$10/1M in · $50/1M out
Laguna XS 2.1 saves approximately $61.24 / month (100%) under this workload.234.3x Savings

Laguna XS 2.1 · Strengths & Key Advantages

  • ultra-cheap tarification ($0.05 / 1M entrée) tailored pour continuous développeur intégration
  • élevé codage précision sur quotidien scripts, boilerplate, et test cases
  • sous-250ms latence ideal pour inline IDE code suggestions
Considerations & Trade-offs
  • pas conçu pour général non-programmation topics ou créatif writing
  • Struggles avec grand-échelle distributed systems architectural planning

GPT-6 Astra · Strengths & Key Advantages

  • Flawless OS-level navigation et utilisation de l'ordinateur automatisation
  • Seamless en temps réel multimodal voix et vision interactivity
  • Dynamic chaîne de pensée qui self-adjusts profondeur based sur query difficulty
  • immense 1M fenêtre de contexte avec instant prefix caching
Considerations & Trade-offs
  • Strict sécurité filters peut occasionally throttle complexe penetration testing prompts
  • Slightly supérieur raisonnement latence sur élevé-effort mathématiques queries

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricLaguna XS 2.1GPT-6 Astra
Cost per Task
Estimated USD to finish one task
$0.01$2.15
Output Tokens / Task
Tokens generated per task
12K58K
Reasoning Tokens / Task
Thinking tokens burned per task
0K32K

Spécifications Techniques

Architecture, release timeline, capabilities, and modalities.

CaractéristiqueLaguna XS 2.1GPT-6 Astra
Official API Model ID (SDK)laguna-xs-2-1gpt-6-astra
API ProtocolPoolside REST API (OpenAI-Compatible)OpenAI Responses API & Chat Completions v1
Release Date2026-072026-09
Knowledge CutoffMay 2026June 2026
ArchitectureUltra-Fast Code Optimization TransformerOmni-Adaptive Autoregressive with Dynamic CoT
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($0.05/1M in) across full 262K windowFlat rate ($10/1M in) across full 272K window
Prompt Cache Policy & TTLCached input at $0.005/1M tokensAutomatic prefix caching at $1/1M (no extra cache-write fee)
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportAPI Prompt Engineering & RAG DistillationSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token BillingText BPE (~4 chars/token)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec)
Quantization & Min GPU VRAMCloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier ClassificationPoolside Production Safety & Alignment VerifiedOpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestexttext, vision, audio
Output Modalitiestexttext, audio

Laguna XS 2.1 vs GPT-6 Astra: Réponses Rapides (FAQ)

Which is more intelligent, Laguna XS 2.1 or GPT-6 Astra?

GPT-6 Astra is more intelligent. It scores 96 compared with 77 on the composite Intelligence Index.

Which model is faster?

Laguna XS 2.1 is faster, generating 165 tokens per second compared with 100 tokens per second.

Which model is cheaper to run?

Laguna XS 2.1 is cheaper at $0.03 per 1M tokens blended, compared with $7.70 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Laguna XS 2.1 has lower latency, with a time to first token of 0.24s compared with 0.95s.

Which model has a larger context window?

GPT-6 Astra has a larger context window, supporting 272K tokens compared with 262.1K tokens.

Verdict

Choose Laguna XS 2.1 for ide autocompletion, ci/cd automatisé test génération, et pull request syntax checking, or choose GPT-6 Astra for autonome browser/computer flux de travail, en temps réel voix agents, et élevé-difficulty mathématique raisonnement.

Questions fréquentes

Which is cheaper: Laguna XS 2.1 or GPT-6 Astra?+

Laguna XS 2.1 costs $0.05/1M input and $0.2/1M output tokens, while GPT-6 Astra costs $10/1M input and $50/1M output tokens.

Which model has a larger context window: Laguna XS 2.1 or GPT-6 Astra?+

Laguna XS 2.1 supports 262K tokens whereas GPT-6 Astra supports 272K tokens.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.

Sources: Poolside & OpenAI Official Documentation