Head-to-Head Model Comparison

GPT-6 Sol vs Aya Expanse 32B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale
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OpenAICohereflagshipopen-source

Compare GPT-6 Sol by OpenAI against Aya Expanse 32B by Cohere across real-world coding, reasoning, token economics, and latency.

Confronto Diretto (VS)

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OpenAI

GPT-6 Sol

Modello di Ragionamento Profondo

GPT-6 Sol di OpenAI è un modello di intelligenza artificiale con finestra di contesto da 1M token e tariffazione API di $2.0/1M token in input e $10.0/1M in output, ideale per coding, ragionamento e automazione aziendale.

Intelligence Index93 / 100
Input Token:

$2.00 / 1M

Output Token:

$10.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

82.1%

Cohere

Aya Expanse 32B

Modello a Pesi Aperti

Aya Expanse 32B di Cohere è un modello di intelligenza artificiale con finestra di contesto da 128K token e tariffazione API di $0.25/1M token in input e $0.5/1M in output, ideale per coding, ragionamento e automazione aziendale.

Intelligence Index44 / 100
Input Token:

$0.25 / 1M

Output Token:

$0.50 / 1M

Context Window:

128K tokens

SWE-bench Verified:

33.5%

Differenze Chiave a Colpo d’Occhio

  • Aya Expanse 32B is 13.3x cheaper for API usage (input + output tokens combined).
  • GPT-6 Sol has a larger context window (1M vs 128K tokens).
  • GPT-6 Sol is faster at 116 tok/s vs 85 tok/s.
  • GPT-6 Sol leads on SWE-bench coding (82.1% vs 33.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGPT-6 SolAya Expanse 32B
Intelligence Index
Composite reasoning & knowledge score (0-100)
9344
Provider
OpenAICohere
Input Price
Cost per million input tokens
$2.00 / 1M$0.25 / 1M
Output Price
Cost per million output tokens
$10.00 / 1M$0.50 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$1.47 / 1M$0.14 / 1M
Context Window
Maximum input + output tokens
1M tokens~1,500 A4 pages of text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
128K8K
Throughput Speed
Tokens generated per second
116 tok/s85 tok/s
SWE-bench Verified
Real-world software engineering
82.1%33.5%
GPQA Diamond
Graduate-level reasoning
80.6%50%
Cached Input Price
Prompt caching discount per 1M tokens
$0.10 / 1M$0.06 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework
Coding
GPT-6 Sol
+48.6% SWE-bench lead
Cost
Aya Expanse 32B
13.3x cheaper monthly runtime
Speed
GPT-6 Sol
+36% faster generation
Context
GPT-6 Sol
1M tokens window

Punteggi Benchmark Verificati

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

GPT-6 SolAya Expanse 32B
SWE-bench Verified (Autonomous Coding)

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

82.1%vs33.5%
GPQA Diamond (Doctorate-Level Science & Math)

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

80.6%vs50%
MMLU-Pro (Multi-task Language Understanding)

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

89.8%vs73%
LMSYS Chatbot Arena Elo

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

1516ptsvs1265pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

18%vsN/A
Terminal-Bench (Agentic Coding & Terminal Use)

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

63.2%vsN/A
SciCode (Scientific Computing)

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

54.5%vsN/A
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

79.5%vsN/A
AIME (Advanced Math Competition)

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

93.4%vsN/A
Long-Context Reasoning

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

90.8%vs72%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

116 tok/s vs85 tok/s

Latenza e Tempo di Risposta

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
GPT-6 Sol1.16s
Aya Expanse 32B0.44s
Est. End-to-End Response (500 tokens)TTFT + generation time
GPT-6 Sol5.47s
Aya Expanse 32B6.32s

Simulatore Interattivo dei Costi Token

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)
GPT-6 Sol Monthly API Bill
$12.15 / month
$2/1M in · $10/1M out
Aya Expanse 32B Monthly API Bill
$0.92 / month
$0.25/1M in · $0.5/1M out
Aya Expanse 32B saves approximately $11.23 / month (92%) under this workload.13.3x Savings

GPT-6 Sol · Strengths & Key Advantages

  • Architettura Cost-efficient frontier unified reasoning model avanzata di OpenAI con supporto fino a 1M token di contesto
  • Ottimo rapporto qualità-prezzo ($2.0/1M input, $10.0/1M output) e bassa latenza
  • Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
  • L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
  • Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato

Aya Expanse 32B · Strengths & Key Advantages

  • Architettura High-Capacity Multilingual Transformer avanzata di Cohere con supporto fino a 128K token di contesto
  • Ottimo rapporto qualità-prezzo ($0.25/1M input, $0.5/1M output) e bassa latenza
  • Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
  • L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
  • Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato

Token Use & Task Economics

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

MetricGPT-6 SolAya Expanse 32B
Cost per Task
Estimated USD to finish one task
$2.00$0.18
Output Tokens / Task
Tokens generated per task
62K28K
Reasoning Tokens / Task
Thinking tokens burned per task
36K0K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

FeatureGPT-6 SolAya Expanse 32B
Official API Model ID (SDK)gpt-6-solaya-expanse-32b
API ProtocolOpenAI Responses API & Chat Completions v1OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-092024-10
Knowledge CutoffApril 2026August 2024
ArchitectureCost-efficient frontier unified reasoning modelHigh-Capacity Multilingual Transformer
ParametersConfidential32B
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPMUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($2/1M in) across full 1000K windowFlat rate ($0.25/1M in) across full 128K window
Prompt Cache Policy & TTLAutomatic prefix caching at $0.1/1M (no extra cache-write fee)Cached input at $0.06/1M tokens
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
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 & SandboxNative Web Search tool + Hosted Code Interpreter & File Search RAGSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMCloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 20 GB VRAM (4-bit Quantized) · 69 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpenAI Preparedness Framework Audited & System Card PublishedOpen-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext
Output Modalitiestexttext

GPT-6 Sol vs Aya Expanse 32B: Risposte Rapide (FAQ)

Which is more intelligent, GPT-6 Sol or Aya Expanse 32B?

GPT-6 Sol is more intelligent. It scores 93 compared with 44 on the composite Intelligence Index.

Which model is faster?

GPT-6 Sol is faster, generating 116 tokens per second compared with 85 tokens per second.

Which model is cheaper to run?

Aya Expanse 32B is cheaper at $0.14 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Aya Expanse 32B has lower latency, with a time to first token of 0.44s compared with 1.16s.

Which model has a larger context window?

GPT-6 Sol has a larger context window, supporting 1M tokens compared with 128K tokens.

Verdetto

Choose GPT-6 Sol for automazione enterprise, sviluppo software, agenti ia autonomi e pipeline di produzione openai, or choose Aya Expanse 32B for automazione enterprise, sviluppo software, agenti ia autonomi e pipeline di produzione cohere.

Domande Frequenti (FAQ)

Which is cheaper: GPT-6 Sol or Aya Expanse 32B?+

GPT-6 Sol costs $2/1M input and $10/1M output tokens, while Aya Expanse 32B costs $0.25/1M input and $0.5/1M output tokens.

Which model has a larger context window: GPT-6 Sol or Aya Expanse 32B?+

GPT-6 Sol supports 1M tokens whereas Aya Expanse 32B supports 128K 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.

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

Fonti ufficiali: OpenAI & Cohere Official Documentation