Devstral Medium vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Devstral Medium by Mistral AI against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
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Devstral Medium
Deep-Reasoning-ModellDevstral Medium von Mistral AI ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 128K Token zu API-Preisen von $0.8/1M Input- und $2.4/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.80 / 1M
$2.40 / 1M
128K tokens
51.5%
Gemini 4 Argon
Deep-Reasoning-ModellGemini 4 Argon von Google ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 1M Token zu API-Preisen von $2/1M Input- und $10/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Wichtigste Unterschiede auf einen Blick
- Devstral Medium is 3.3x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon has a larger context window (1M vs 128K tokens).
- Gemini 4 Argon is faster at 115 tok/s vs 75 tok/s.
- Gemini 4 Argon leads on SWE-bench coding (81.9% vs 51.5%).
| Feature | Devstral Medium | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 52 | 97 |
Provider | Mistral AI | |
Input Price Cost per million input tokens | $0.80 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $2.40 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.54 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 33K | 1000K |
Throughput Speed Tokens generated per second | 75 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | 51.5% | 81.9% |
GPQA Diamond Graduate-level reasoning | 62% | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.20 / 1M | $0.10 / 1M |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkGeprüfte 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.
Devstral Medium · Strengths & Key Advantages
- Fortschrittliche High-Capacity Agentic Code Transformer-Architektur von Mistral AI mit 128K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0.8/1M Input, $2.4/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Gemini 4 Argon · Strengths & Key Advantages
- Fortschrittliche Pathways Multimodal Sparse Mixture-of-Experts-Architektur von Google mit 1M Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($2/1M Input, $10/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Devstral Medium | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.55 | $2.45 |
Output Tokens / Task Tokens generated per task | 38K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 18K | 36K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | Devstral Medium | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | devstral-medium-latest | models/gemini-4-argon |
| API Protocol | Mistral AI REST API (OpenAI-Compatible) | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2025-07 | 2026-09 |
| Knowledge Cutoff | May 2025 | June 2026 |
| Architecture | High-Capacity Agentic Code Transformer | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | Confidential (~70B) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.8/1M in) across full 128K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.2/1M tokens | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | Vertex AI Supervised Adapter Tuning & Context Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| 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 | Mistral AI Production Safety & Alignment Verified | Google DeepMind Frontier Safety Framework Verified |
| 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 | text, vision, audio, video |
| Output Modalities | text | text |
Devstral Medium vs Gemini 4 Argon: Schnelle Antworten (FAQ)
Which is more intelligent, Devstral Medium or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 52 on the composite Intelligence Index.
Which model is faster?
Gemini 4 Argon is faster, generating 115 tokens per second compared with 75 tokens per second.
Which model is cheaper to run?
Devstral Medium is cheaper at $0.54 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Devstral Medium has lower latency, with a time to first token of 0.50s compared with 0.85s.
Which model has a larger context window?
Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 128K tokens.
Fazit
Choose Devstral Medium for unternehmensautomatisierung, software-engineering, autonome ki-agenten und mistral ai-produktionssysteme, or choose Gemini 4 Argon for unternehmensautomatisierung, software-engineering, autonome ki-agenten und google-produktionssysteme.
Häufig gestellte Fragen (FAQ)
Which is cheaper: Devstral Medium or Gemini 4 Argon?+
Devstral Medium costs $0.8/1M input and $2.4/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Devstral Medium or Gemini 4 Argon?+
Devstral Medium supports 128K tokens whereas Gemini 4 Argon 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: Mistral AI & Google Official Documentation