Head-to-Head Model Comparison

GPT-5.5 vs GPT-5.6 Sol

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
GPT-5.5 vs GPT-5.6 Sol: Benchmarks, Pricing & Speed Comparison

GPT-5.5 by OpenAI and GPT-5.6 Sol by OpenAI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, GPT-5.6 Sol holds a notable pricing advantage for token ingestion. On output generation, GPT-5.6 Sol is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 1,050K tokens. In latency-sensitive production environments, GPT-5.5 leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.

Perbandingan Langsung (VS)

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OpenAI

GPT-5.5

Model Penalaran Mendalam

GPT-5.5 dari OpenAI adalah model AI canggih dengan jendela konteks 1.1M token serta harga API $5/1M token input dan $30/1M token output untuk pemrograman, penalaran, dan otomasi bisnis.

Intelligence Index84 / 100
Input Token:

$5.00 / 1M

Output Token:

$30.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

81%

OpenAI

GPT-5.6 Sol

Model Penalaran Mendalam

GPT-5.6 Sol dari OpenAI adalah model AI canggih dengan jendela konteks 1.1M token serta harga API $4/1M token input dan $20/1M token output untuk pemrograman, penalaran, dan otomasi bisnis.

Intelligence Index89 / 100
Input Token:

$4.00 / 1M

Output Token:

$20.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

83.8%

Perbedaan Utama Sekilas

  • GPT-5.6 Sol is 1.4x cheaper for API usage (input + output tokens combined).
  • GPT-5.5 is faster at 112 tok/s vs 108 tok/s.
  • GPT-5.6 Sol leads on SWE-bench coding (83.8% vs 81.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGPT-5.5GPT-5.6 Sol
Intelligence Index
Composite reasoning & knowledge score (0-100)
8489
Provider
OpenAIOpenAI
Input Price
Cost per million input tokens
$5.00 / 1M$4.00 / 1M
Output Price
Cost per million output tokens
$30.00 / 1M$20.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$4.35 / 1M$3.08 / 1M
Context Window
Maximum input + output tokens
1.05M tokens~1,575 A4 pages of text1.05M tokens~1,575 A4 pages of text
Max Output Tokens
Per single request
128K128K
Throughput Speed
Tokens generated per second
112 tok/s108 tok/s
SWE-bench Verified
Real-world software engineering
81%83.8%
GPQA Diamond
Graduate-level reasoning
80%82%
Cached Input Price
Prompt caching discount per 1M tokens
$0.50 / 1M$0.40 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework
Coding
GPT-5.6 Sol
+2.8% SWE-bench lead
Cost
GPT-5.6 Sol
1.4x cheaper monthly runtime
Speed
GPT-5.5
+4% faster generation
Context
Tied
1.05M tokens window

Skor Benchmark Terverifikasi

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

GPT-5.5GPT-5.6 Sol
SWE-bench Verified (Autonomous Coding)

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

81%vs83.8%
GPQA Diamond (Doctorate-Level Science & Math)

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

80%vs82%
MMLU-Pro (Multi-task Language Understanding)

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

89.6%vs90.8%
LMSYS Chatbot Arena Elo

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

1465ptsvs1496pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

16.2%vs17.8%
Terminal-Bench (Agentic Coding & Terminal Use)

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

57.5%vs61%
SciCode (Scientific Computing)

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

52%vs55%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

78.5%vs80.6%
AIME (Advanced Math Competition)

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

94.8%vs95.8%
Long-Context Reasoning

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

87%vs89.6%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

112 tok/s vs108 tok/s

Latensi & Waktu Respons

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-5.51.18s
GPT-5.6 Sol1.21s
Est. End-to-End Response (500 tokens)TTFT + generation time
GPT-5.55.64s
GPT-5.6 Sol5.84s

Simulator Biaya Token Interaktif

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-5.5 Monthly API Bill
$35.25 / month
$5/1M in · $30/1M out
GPT-5.6 Sol Monthly API Bill
$24.60 / month
$4/1M in · $20/1M out
GPT-5.6 Sol saves approximately $10.65 / month (30%) under this workload.1.4x Savings

GPT-5.5 · Strengths & Key Advantages

  • Arsitektur Default frontier unified reasoning model mutakhir dari OpenAI dengan dukungan konteks 1.1M token
  • Efisiensi biaya API yang kompetitif ($5/1M input, $30/1M output) dan respons cepat
  • Performa andal dalam pembuatan kode, analisis logika, serta pemahaman multibahasa
Considerations & Trade-offs
  • Penggunaan konteks berukuran penuh memerlukan pengelolaan anggaran token yang cermat
  • Membutuhkan perancangan prompt yang tepat untuk kasus penggunaan industri spesifik

GPT-5.6 Sol · Strengths & Key Advantages

  • Arsitektur Frontier unified reasoning model mutakhir dari OpenAI dengan dukungan konteks 1.1M token
  • Efisiensi biaya API yang kompetitif ($4/1M input, $20/1M output) dan respons cepat
  • Performa andal dalam pembuatan kode, analisis logika, serta pemahaman multibahasa
Considerations & Trade-offs
  • Penggunaan konteks berukuran penuh memerlukan pengelolaan anggaran token yang cermat
  • Membutuhkan perancangan prompt yang tepat untuk kasus penggunaan industri spesifik

Token Use & Task Economics

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

MetricGPT-5.5GPT-5.6 Sol
Cost per Task
Estimated USD to finish one task
$3.90$3.30
Output Tokens / Task
Tokens generated per task
64K64K
Reasoning Tokens / Task
Thinking tokens burned per task
37K38K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

FeatureGPT-5.5GPT-5.6 Sol
Official API Model ID (SDK)gpt-5-5gpt-5-6-sol
API ProtocolOpenAI Responses API & Chat Completions v1OpenAI Responses API & Chat Completions v1
Release Date2026-042026-07
Knowledge CutoffDecember 2025February 2026
ArchitectureDefault frontier unified reasoning modelFrontier unified reasoning model
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($5/1M in) across full 1050K windowFlat rate ($4/1M in) across full 1050K window
Prompt Cache Policy & TTLAutomatic prefix caching at $0.5/1M (no extra cache-write fee)Automatic prefix caching at $0.4/1M (no extra cache-write fee)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)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 & SandboxNative Web Search tool + Hosted Code Interpreter & File Search RAGNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
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 ClassificationOpenAI Preparedness Framework Audited & System Card PublishedOpenAI 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 Modalitiestext, visiontext, vision
Output Modalitiestexttext

GPT-5.5 vs GPT-5.6 Sol: Jawaban Cepat (FAQ)

Which is more intelligent, GPT-5.5 or GPT-5.6 Sol?

GPT-5.6 Sol is more intelligent. It scores 89 compared with 84 on the composite Intelligence Index.

Which model is faster?

GPT-5.5 is faster, generating 112 tokens per second compared with 108 tokens per second.

Which model is cheaper to run?

GPT-5.6 Sol is cheaper at $3.08 per 1M tokens blended, compared with $4.35 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GPT-5.5 has lower latency, with a time to first token of 1.18s compared with 1.21s.

Kesimpulan

Verdict & Model Selection Recommendation: Choose GPT-5.5 if your priority is Frontier agents and demanding professional knowledge work. Its performance in demanding tasks justifies its operational token cost. Choose GPT-5.6 Sol if your workflow prioritizes Complex professional work and agentic coding pipelines. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.

Pertanyaan Umum (FAQ)

How does GPT-5.5 compare to GPT-5.6 Sol in API token pricing and cost per token?+

GPT-5.5 costs $5.00 per 1M input tokens and $30.00 per 1M output tokens, whereas GPT-5.6 Sol costs $4.00 input and $20.00 output. Overall, GPT-5.6 Sol is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.

Which model has a larger context window: GPT-5.5 or GPT-5.6 Sol?+

Both GPT-5.5 and GPT-5.6 Sol offer an identical context window of 1,050K tokens (~788k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.

Which AI model is better for software engineering and coding: GPT-5.5 or GPT-5.6 Sol?+

GPT-5.5 scores 81% on SWE-bench Verified while GPT-5.6 Sol scores 83.8%. For autonomous software engineering and multi-file debugging, GPT-5.6 Sol holds the verified benchmark lead.

What is the difference in reasoning capability and extended thinking between GPT-5.5 and GPT-5.6 Sol?+

GPT-5.5 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-5.6 Sol provides chain-of-thought reasoning depth for complex math and science.

How does inference speed (tokens per second) compare between GPT-5.5 and GPT-5.6 Sol?+

GPT-5.5 generates approximately 112 tokens per second, while GPT-5.6 Sol delivers 108 tokens per second. In latency-sensitive production environments, GPT-5.5 provides faster time-to-first-token (TTFT) and higher streaming response velocity.

Can you implement a tiered LLM routing setup combining GPT-5.5 and GPT-5.6 Sol to optimize costs?+

Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-5.6 Sol to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-5.5. This tiered routing pattern cuts overall API bills by up to 70%.

AI Terminology & Guide
Explore Glossary →

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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.

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