Gemini 3.5 Flash-Lite vs GPT-6.1 Sol
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemini 3.5 Flash-Lite by Google against GPT-6.1 Sol by OpenAI across real-world coding, reasoning, token economics, and latency.
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Gemini 3.5 Flash-Lite
Ultraschnelle Echtzeit-InferenzGemini 3.5 Flash-Lite von Google ist ein KI-Modell für ultraschnelle Echtzeit-Antworten, niedrige Latenz und Hochdurchsatz-Routing mit 1.0M Kontextfenster, 240 Token/s Durchsatz, einem Intelligenz-Index von 69/100 und $0.4/$2 pro 1M Token.
$0.40 / 1M
$2.00 / 1M
1.05M tokens
N/A
GPT-6.1 Sol
Frontier-Flaggschiff-ModellGPT-6.1 Sol von OpenAI ist ein KI-Modell für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows mit 1M Kontextfenster, 120 Token/s Durchsatz, einem Intelligenz-Index von 94/100 und $2/$10 pro 1M Token.
$2.00 / 1M
$10.00 / 1M
1M tokens
82.3%
Wichtigste Unterschiede auf einen Blick
- Gemini 3.5 Flash-Lite is 4.8x cheaper for API usage (input + output tokens combined).
- Gemini 3.5 Flash-Lite has a larger context window (1M vs 1M tokens).
- Gemini 3.5 Flash-Lite is faster at 240 tok/s vs 120 tok/s.
| Feature | Gemini 3.5 Flash-Lite | GPT-6.1 Sol |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 69 | 94 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $0.40 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $2.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.35 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,573 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 33K | 128K |
Throughput Speed Tokens generated per second | 240 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 82.3% |
GPQA Diamond Graduate-level reasoning | N/A | 80.8% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.10 / 1M |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkEvaluations ongoing
Geprüfte Benchmark-Ergebnisse im Vergleich
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.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini 3.5 Flash-Lite · Strengths & Key Advantages
- 1.0M Kontextfenster und bis zu 33K maximale Output-Token (MMLU-Pro 81.2% · Arena Elo 1375)
- Hohe Generierungsgeschwindigkeit von 240 Token/Sekunde bei nur 290 ms Erstlatenz (TTFT)
- Effiziente API-Struktur ($0.4/$2 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe
Considerations & Trade-offs
- Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
- Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen
GPT-6.1 Sol · Strengths & Key Advantages
- 1M Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 82.3% · GPQA Diamond 80.8%)
- Hohe Generierungsgeschwindigkeit von 120 Token/Sekunde bei nur 1120 ms Erstlatenz (TTFT)
- Effiziente API-Struktur ($2/$10 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe
Considerations & Trade-offs
- Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
- Bei hohem Anfragevolumen empfiehlt sich der Einsatz von Prompt-Caching und Batch-API zur Kostenoptimierung
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini 3.5 Flash-Lite | GPT-6.1 Sol |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.55 | $2.05 |
Output Tokens / Task Tokens generated per task | 18K | 62K |
Reasoning Tokens / Task Thinking tokens burned per task | 7K | 36K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini 3.5 Flash-Lite | GPT-6.1 Sol |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-3-5-flash-lite | gpt-6-1-sol |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-07 | 2026-09 |
| Knowledge Cutoff | March 2026 | April 2026 |
| Architecture | Lightweight efficient multimodal transformer | Near-Astra 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 1000K window |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL | Automatic prefix caching at $0.1/1M (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) | 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 | text, vision |
| Output Modalities | text | text |
Gemini 3.5 Flash-Lite vs GPT-6.1 Sol: Schnelle Antworten (FAQ)
Which is more intelligent, Gemini 3.5 Flash-Lite or GPT-6.1 Sol?
GPT-6.1 Sol is more intelligent. It scores 94 compared with 69 on the composite Intelligence Index.
Which model is faster?
Gemini 3.5 Flash-Lite is faster, generating 240 tokens per second compared with 120 tokens per second.
Which model is cheaper to run?
Gemini 3.5 Flash-Lite is cheaper at $0.35 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 3.5 Flash-Lite has lower latency, with a time to first token of 0.29s compared with 1.12s.
Which model has a larger context window?
Gemini 3.5 Flash-Lite has a larger context window, supporting 1.05M tokens compared with 1M tokens.
Fazit
Choose Gemini 3.5 Flash-Lite for optimal für ultraschnelle echtzeit-antworten, niedrige latenz und hochdurchsatz-routing, enterprise-rag-systeme und skalierbare api-pipelines., or choose GPT-6.1 Sol for optimal für anspruchsvolles enterprise-reasoning, softwareentwicklung und produktive ki-workflows, enterprise-rag-systeme und skalierbare api-pipelines..
Häufig gestellte Fragen (FAQ)
Which is cheaper: Gemini 3.5 Flash-Lite or GPT-6.1 Sol?+
Gemini 3.5 Flash-Lite costs $0.4/1M input and $2/1M output tokens, while GPT-6.1 Sol costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Gemini 3.5 Flash-Lite or GPT-6.1 Sol?+
Gemini 3.5 Flash-Lite supports 1.0M tokens whereas GPT-6.1 Sol supports 1M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI 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.
Offizielle Quellen: Google & OpenAI Official Documentation