Gemini 3.7 Flash vs GPT-5.6 Terra
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Gemini 3.7 Flash dari Google dan GPT-5.6 Terra dari OpenAI adalah dua model AI unggulan. Gemini 3.7 Flash menawarkan jendela konteks 1.05M token dengan harga $0.75/$3.75 per 1 juta token, sedangkan GPT-5.6 Terra menyediakan konteks 1.05M token seharga $2.00/$10.00 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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Gemini 3.7 Flash
Model Unggulan Garis DepanGemini 3.7 Flash dari Google adalah model AI yang dirancang untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, menghadirkan jendela konteks 1.0M, kecepatan 180 token/detik, indeks kecerdasan 85/100, serta t
$0.75 / 1M
$3.75 / 1M
1.05M tokens
73.4%
GPT-5.6 Terra
Model Unggulan Garis DepanGPT-5.6 Terra dari OpenAI adalah model AI yang dirancang untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, menghadirkan jendela konteks 1.1M, kecepatan 124 token/detik, indeks kecerdasan 85/100, serta tari
$2.00 / 1M
$10.00 / 1M
1.05M tokens
81.4%
Perbedaan Utama Sekilas
- Gemini 3.7 Flash is 2.6x cheaper for API usage (input + output tokens combined).
- GPT-5.6 Terra has a larger context window (1M vs 1M tokens).
- Gemini 3.7 Flash is faster at 180 tok/s vs 124 tok/s.
- GPT-5.6 Terra leads on SWE-bench coding (81.4% vs 73.4%).
| Feature | Gemini 3.7 Flash | GPT-5.6 Terra |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 85 | 85 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $0.75 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $3.75 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.66 / 1M | $1.54 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,573 A4 pages of text | 1.05M tokens~1,575 A4 pages of text |
Max Output Tokens Per single request | 66K | 128K |
Throughput Speed Tokens generated per second | 180 tok/s | 124 tok/s |
SWE-bench Verified Real-world software engineering | 73.4% | 81.4% |
GPQA Diamond Graduate-level reasoning | 83.1% | 80.4% |
Cached Input Price Prompt caching discount per 1M tokens | $0.19 / 1M | $0.20 / 1M |
Siapa Unggul di Mana?
At-a-glance decision frameworkSkor Benchmark Terverifikasi
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini 3.7 Flash · Strengths & Key Advantages
- Jendela konteks 1.0M dengan kapasitas output maksimum hingga 66K token (SWE-bench Verified 73.4% · GPQA Diamond 83.1%)
- Kecepatan generasi tinggi 180 token/detik dengan latensi token pertama (TTFT) hanya 380 ms
- Skema harga API kompetitif (tarif $0.75/$3.75 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
- Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
GPT-5.6 Terra · Strengths & Key Advantages
- Jendela konteks 1.1M dengan kapasitas output maksimum hingga 128K token (SWE-bench Verified 81.4% · GPQA Diamond 80.4%)
- Kecepatan generasi tinggi 124 token/detik dengan latensi token pertama (TTFT) hanya 1090 ms
- Skema harga API kompetitif (tarif $2/$10 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur
Considerations & Trade-offs
- Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
- Untuk trafik produksi skala besar, disarankan mengaktifkan fitur Prompt Caching dan Batch API guna menghemat biaya
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini 3.7 Flash | GPT-5.6 Terra |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.15 | $1.95 |
Output Tokens / Task Tokens generated per task | 42K | 58K |
Reasoning Tokens / Task Thinking tokens burned per task | 20K | 32K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini 3.7 Flash | GPT-5.6 Terra |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-3-7-flash | gpt-5-6-terra |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-08 | 2026-07 |
| Knowledge Cutoff | May 2026 | February 2026 |
| Architecture | Agent-first adaptive multimodal transformer | Balanced unified reasoning model |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Flat rate ($2/1M in) across full 1050K window |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0.1875/1M with configurable TTL | Automatic prefix caching at $0.2/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context Distillation | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| Quantization & Min GPU VRAM | Cloud-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 Classification | Google DeepMind Frontier Safety Framework Verified | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision, audio, video | text, vision |
| Output Modalities | text | text |
Gemini 3.7 Flash vs GPT-5.6 Terra: Jawaban Cepat (FAQ)
Which model is faster?
Gemini 3.7 Flash is faster, generating 180 tokens per second compared with 124 tokens per second.
Which model is cheaper to run?
Gemini 3.7 Flash is cheaper at $0.66 per 1M tokens blended, compared with $1.54 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 3.7 Flash has lower latency, with a time to first token of 0.38s compared with 1.09s.
Which model has a larger context window?
GPT-5.6 Terra has a larger context window, supporting 1.05M tokens compared with 1.05M tokens.
Kesimpulan
Baik Gemini 3.7 Flash (Sangat cocok untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun GPT-5.6 Terra (Sangat cocok untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Gemini 3.7 Flash unggul dalam penalaran dan pemrograman, Gemini 3.7 Flash memberikan kecepatan generasi token lebih tinggi, dan Gemini 3.7 Flash lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Gemini 3.7 Flash atau GPT-5.6 Terra?+
Dalam indeks kecerdasan gabungan dan benchmark coding, Gemini 3.7 Flash memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Gemini 3.7 Flash vs GPT-5.6 Terra?+
Gemini 3.7 Flash mengenakan biaya $0.75 / $3.75 per 1M token input/output, sedangkan GPT-5.6 Terra sebesar $2.00 / $10.00. Secara keseluruhan, Gemini 3.7 Flash lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Gemini 3.7 Flash mendukung konteks 1.05M token dibandingkan 1.05M token pada GPT-5.6 Terra, sementara Gemini 3.7 Flash unggul dalam kecepatan output token per detik.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- Gemini 3.7 Flash vs Gemini 3.6 Flash: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
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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.
Sumber resmi: Dokumentasi API resmi Google & OpenAI serta evaluasi benchmark independen (2026).