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Head-to-Head Model Comparison

GPT-6 Sol vs Aya Expanse 8B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs अनुसंधान दल द्वारा समीक्षितअंतिम समीक्षा: October 2026संपादकीय नीति
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Compare GPT-6 Sol by OpenAI against Aya Expanse 8B by Cohere across real-world coding, reasoning, token economics, and latency.

आमने-सामने मॉडल तुलना (VS)

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

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OpenAI

GPT-6 Sol

डीप रीजनिंग मॉडल

OpenAI द्वारा विकसित GPT-6 Sol एक उन्नत AI मॉडल है, जिसमें 1M टोकन का कॉन्टेक्स्ट विंडो और $2.0/1M इनपुट तथा $10.0/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।

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 8B

ओपन-वेट्स मॉडल

Cohere द्वारा विकसित Aya Expanse 8B एक उन्नत AI मॉडल है, जिसमें 8K टोकन का कॉन्टेक्स्ट विंडो और $0.08/1M इनपुट तथा $0.16/1M आउटपुट मूल्य निर्धारण है। यह कोडिंग, विश्लेषण और एंटरप्राइज वर्कफ़्लो के लिए उपयुक्त है।

Intelligence Index36 / 100
Input Token:

$0.08 / 1M

Output Token:

$0.16 / 1M

Context Window:

8.2K tokens

SWE-bench Verified:

24%

एक नज़र में मुख्य अंतर

  • Aya Expanse 8B is 41.3x cheaper for API usage (input + output tokens combined).
  • GPT-6 Sol has a larger context window (1M vs 8K tokens).
  • Aya Expanse 8B is faster at 135 tok/s vs 116 tok/s.
  • GPT-6 Sol leads on SWE-bench coding (82.1% vs 24.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGPT-6 SolAya Expanse 8B
Intelligence Index
Composite reasoning & knowledge score (0-100)
9336
Provider
OpenAICohere
Input Price
Cost per million input tokens
$2.00 / 1M$0.08 / 1M
Output Price
Cost per million output tokens
$10.00 / 1M$0.16 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$1.47 / 1M$0.05 / 1M
Context Window
Maximum input + output tokens
1M tokens~1,500 A4 pages of text8.2K tokens~12 A4 pages of text
Max Output Tokens
Per single request
128K4K
Throughput Speed
Tokens generated per second
116 tok/s135 tok/s
SWE-bench Verified
Real-world software engineering
82.1%24%
GPQA Diamond
Graduate-level reasoning
80.6%40%
Cached Input Price
Prompt caching discount per 1M tokens
$0.10 / 1M$0.02 / 1M

किस क्षेत्र में कौन आगे है?

At-a-glance decision framework
Coding
GPT-6 Sol
+58.1% SWE-bench lead
Cost
Aya Expanse 8B
41.3x cheaper monthly runtime
Speed
Aya Expanse 8B
+16% faster generation
Context
GPT-6 Sol
1M tokens window

सत्यापित बेंचमार्क स्कोर तुलना

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

GPT-6 SolAya Expanse 8B
SWE-bench Verified (Autonomous Coding)

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

82.1%vs24%
GPQA Diamond (Doctorate-Level Science & Math)

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

80.6%vs40%
MMLU-Pro (Multi-task Language Understanding)

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

89.8%vs62%
LMSYS Chatbot Arena Elo

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

1516ptsvs1205pts
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%vsN/A
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

116 tok/s vs135 tok/s

लेटेंसी और रिस्पॉन्स समय

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 8B0.30s
Est. End-to-End Response (500 tokens)TTFT + generation time
GPT-6 Sol5.47s
Aya Expanse 8B4.00s

रियल-टाइम टोकन लागत सिम्युलेटर

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 8B Monthly API Bill
$0.29 / month
$0.08/1M in · $0.16/1M out
Aya Expanse 8B saves approximately $11.86 / month (98%) under this workload.41.3x Savings

GPT-6 Sol · Strengths & Key Advantages

  • OpenAI की उन्नत Cost-efficient frontier unified reasoning model आर्किटेक्चर और 1M कॉन्टेक्स्ट विंडो क्षमता
  • किफायती और पारदर्शी API मूल्य ($2.0/1M इनपुट, $10.0/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
  • जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
  • बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
  • विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है

Aya Expanse 8B · Strengths & Key Advantages

  • Cohere की उन्नत Compact Multilingual Dense Transformer आर्किटेक्चर और 8K कॉन्टेक्स्ट विंडो क्षमता
  • किफायती और पारदर्शी API मूल्य ($0.08/1M इनपुट, $0.16/1M आउटपुट) तेज़ रिस्पॉन्स के साथ
  • जटिल कोडिंग, तार्किक विश्लेषण और बहुभाषी कार्यों में उच्च सटीकता
Considerations & Trade-offs
  • बड़े पैमाने पर लंबे कॉन्टेक्स्ट प्रॉम्प्ट्स के लिए टोकन बजट प्रबंधन आवश्यक है
  • विशिष्ट डोमेन कार्यों में सर्वोत्तम परिणाम के लिए सटीक प्रॉम्प्ट इंजीनियरिंग की आवश्यकता होती है

Token Use & Task Economics

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

MetricGPT-6 SolAya Expanse 8B
Cost per Task
Estimated USD to finish one task
$2.00$0.04
Output Tokens / Task
Tokens generated per task
62K18K
Reasoning Tokens / Task
Thinking tokens burned per task
36K0K

तकनीकी विशिष्टताएँ (Specs)

Architecture, release timeline, capabilities, and modalities.

FeatureGPT-6 SolAya Expanse 8B
Official API Model ID (SDK)gpt-6-solaya-expanse-8b
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 modelCompact Multilingual Dense Transformer
ParametersConfidential8B
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 ($0.08/1M in) across full 8K window
Prompt Cache Policy & TTLAutomatic prefix caching at $0.1/1M (no extra cache-write fee)Cached input at $0.02/1M 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) · 5 GB VRAM (4-bit Quantized) · 17 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 8B: त्वरित प्रश्नोत्तर (FAQ)

Which is more intelligent, GPT-6 Sol or Aya Expanse 8B?

GPT-6 Sol is more intelligent. It scores 93 compared with 36 on the composite Intelligence Index.

Which model is faster?

Aya Expanse 8B is faster, generating 135 tokens per second compared with 116 tokens per second.

Which model is cheaper to run?

Aya Expanse 8B is cheaper at $0.05 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Aya Expanse 8B has lower latency, with a time to first token of 0.30s compared with 1.16s.

Which model has a larger context window?

GPT-6 Sol has a larger context window, supporting 1M tokens compared with 8.2K tokens.

निष्कर्ष

Choose GPT-6 Sol for एंटरप्राइज ऑटोमेशन, सॉफ्टवेयर डेवलपमेंट, ai एजेंट्स और openai आधारित प्रोडक्शन सिस्टम, or choose Aya Expanse 8B for एंटरप्राइज ऑटोमेशन, सॉफ्टवेयर डेवलपमेंट, ai एजेंट्स और cohere आधारित प्रोडक्शन सिस्टम.

अक्सर पूछे जाने वाले प्रश्न (FAQ)

Which is cheaper: GPT-6 Sol or Aya Expanse 8B?+

GPT-6 Sol costs $2/1M input and $10/1M output tokens, while Aya Expanse 8B costs $0.08/1M input and $0.16/1M output tokens.

Which model has a larger context window: GPT-6 Sol or Aya Expanse 8B?+

GPT-6 Sol supports 1M tokens whereas Aya Expanse 8B supports 8K 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.

Tokens & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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.

स्रोत: OpenAI & Cohere Official Documentation