Gemini 3.8 Flash vs GPT-4.1 mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Gemini 3.8 Flash by Google and GPT-4.1 mini 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-4.1 mini holds a notable pricing advantage for token ingestion. On output generation, GPT-4.1 mini is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Gemini 3.8 Flash provides an expanded memory horizon with 1,049K tokens. In latency-sensitive production environments, Gemini 3.8 Flash 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.
Changez et Comparez les Modèles
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Gemini 3.8 Flash
Dernier Flash stablele définitif stable Flash version de late 2026 powering Gemini Advanced et Google Workspace.
$0.75 / 1M
$3.75 / 1M
1.05M tokens
74.8%
GPT-4.1 mini
Contexte long économiquele économique cut de GPT-4.1 — rapide codage et structuré extraction avec le same 1M-token window.
$0.40 / 1M
$1.60 / 1M
1.05M tokens
36.5%
Différences Clés en un Coup d’Œil
- GPT-4.1 mini is 2.2x cheaper for API usage (input + output tokens combined).
- Gemini 3.8 Flash has a larger context window (1M vs 1M tokens).
- Gemini 3.8 Flash is faster at 175 tok/s vs 165 tok/s.
- Gemini 3.8 Flash leads on SWE-bench coding (74.8% vs 36.5%).
| Caractéristique | Gemini 3.8 Flash | GPT-4.1 mini |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 87 | 36 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $0.75 / 1M | $0.40 / 1M |
Output Price Cost per million output tokens | $3.75 / 1M | $1.60 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.66 / 1M | $0.31 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,573 A4 pages of text | 1.05M tokens~1,571 A4 pages of text |
Max Output Tokens Per single request | 66K | 33K |
Throughput Speed Tokens generated per second | 175 tok/s | 165 tok/s |
SWE-bench Verified Real-world software engineering | 74.8% | 36.5% |
GPQA Diamond Graduate-level reasoning | 84% | 49.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.19 / 1M | $0.10 / 1M |
Qui gagne où ?
At-a-glance decision frameworkScores de Benchmarks Vérifiés
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.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini 3.8 Flash · Strengths & Key Advantages
- Default production modèle pour Google Workspace d'entreprise
- Superb balance de vitesse, intelligence, et prix
- profond multimodal ancrage à travers 1M tokens
Considerations & Trade-offs
- Preceded dans max sortie capacité par Gemini 4 Argon
- supérieur limites de débit apply during sommet cloud hours
GPT-4.1 mini · Strengths & Key Advantages
- 1M-token contexte pour un fraction de GPT-4.1 tarification
- rapide enough pour interactif routage et extraction
- fiable structuré JSON sortie
- solide vision performances pour son prix classe
Considerations & Trade-offs
- Weak sur complexe multi-étapes raisonnement
- Noticeably moins accurate que complet GPT-4.1 sur long documents
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini 3.8 Flash | GPT-4.1 mini |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.20 | $0.14 |
Output Tokens / Task Tokens generated per task | 44K | 48K |
Reasoning Tokens / Task Thinking tokens burned per task | 21K | 0K |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | Gemini 3.8 Flash | GPT-4.1 mini |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-3-8-flash | gpt-4-1-mini |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-09 | 2025-04 |
| Knowledge Cutoff | June 2026 | June 2024 |
| Architecture | Late-2026 optimized sparse multimodal transformer | Dense multimodal transformer (small) |
| 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 ($0.4/1M in) across full 1048K window |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0.1875/1M 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) · 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 3.8 Flash vs GPT-4.1 mini: Réponses Rapides (FAQ)
Which is more intelligent, Gemini 3.8 Flash or GPT-4.1 mini?
Gemini 3.8 Flash is more intelligent. It scores 87 compared with 36 on the composite Intelligence Index.
Which model is faster?
Gemini 3.8 Flash is faster, generating 175 tokens per second compared with 165 tokens per second.
Which model is cheaper to run?
GPT-4.1 mini is cheaper at $0.31 per 1M tokens blended, compared with $0.66 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-4.1 mini has lower latency, with a time to first token of 0.30s compared with 0.39s.
Which model has a larger context window?
Gemini 3.8 Flash has a larger context window, supporting 1.05M tokens compared with 1.05M tokens.
Verdict
Verdict & Model Selection Recommendation: Choose Gemini 3.8 Flash if your priority is Enterprise document synthesis, general coding, and high-quality voice/vision workflows. Its performance in demanding tasks justifies its operational token cost. Choose GPT-4.1 mini if your workflow prioritizes High-volume extraction, classification and agent routing. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Questions fréquentes
How does Gemini 3.8 Flash compare to GPT-4.1 mini in API token pricing and cost per token?+
Gemini 3.8 Flash costs $0.75 per 1M input tokens and $3.75 per 1M output tokens, whereas GPT-4.1 mini costs $0.40 input and $1.60 output. Overall, GPT-4.1 mini 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: Gemini 3.8 Flash or GPT-4.1 mini?+
Gemini 3.8 Flash supports a context window of 1,049K tokens (~786k words), while GPT-4.1 mini supports 1,048K tokens (~786k words). Therefore, Gemini 3.8 Flash can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Gemini 3.8 Flash or GPT-4.1 mini?+
Gemini 3.8 Flash scores 74.8% on SWE-bench Verified while GPT-4.1 mini scores 36.5%. For autonomous software engineering and multi-file debugging, Gemini 3.8 Flash holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Gemini 3.8 Flash and GPT-4.1 mini?+
Gemini 3.8 Flash features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-4.1 mini focuses on standard fast inference.
How does inference speed (tokens per second) compare between Gemini 3.8 Flash and GPT-4.1 mini?+
Gemini 3.8 Flash generates approximately 175 tokens per second, while GPT-4.1 mini delivers 165 tokens per second. In latency-sensitive production environments, Gemini 3.8 Flash provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Gemini 3.8 Flash and GPT-4.1 mini to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-4.1 mini to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Gemini 3.8 Flash. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: GetInfoUs Independent AI Benchmarks & Official API Specs