Kimi K2.5 vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Kimi K2.5 by Moonshot AI against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
Beliebige Modelle wechseln & vergleichen
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Live comparison: no in-depth editorial review has been published for this pair yet.
Kimi K2.5
Nativ Multimodale KIKimi K2.5 von Moonshot AI ist ein KI-Modell für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse mit 262K Kontextfenster, 65 Token/s Durchsatz, einem Intelligenz-Index von 86/100 und kostenlosem Open-Weights
$0.00 / 1M
$0.00 / 1M
262.1K tokens
52%
Gemini 4 Argon
Nativ Multimodale KIGemini 4 Argon von Google ist ein KI-Modell für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse mit 1M Kontextfenster, 115 Token/s Durchsatz, einem Intelligenz-Index von 97/100 und $2/$10 pro 1M Token.
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Wichtigste Unterschiede auf einen Blick
- Kimi K2.5 is 1.0x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon has a larger context window (1M vs 262K tokens).
- Gemini 4 Argon is faster at 115 tok/s vs 65 tok/s.
- Gemini 4 Argon leads on SWE-bench coding (81.9% vs 52.0%).
| Feature | Kimi K2.5 | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 86 | 97 |
Provider | Moonshot AI | |
Input Price Cost per million input tokens | $0.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 16K | 1000K |
Throughput Speed Tokens generated per second | 65 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | 52% | 81.9% |
GPQA Diamond Graduate-level reasoning | 69.5% | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 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.
Kimi K2.5 · Strengths & Key Advantages
- 262K Kontextfenster und bis zu 16K maximale Output-Token (SWE-bench Verified 52% · GPQA Diamond 69.5%)
- Hohe Generierungsgeschwindigkeit von 65 Token/Sekunde bei nur 620 ms Erstlatenz (TTFT)
- Offene Modellgewichte (1.2T total (36B MoE)) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama
Considerations & Trade-offs
- Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
- Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen
Gemini 4 Argon · Strengths & Key Advantages
- 1M Kontextfenster und bis zu 1M maximale Output-Token (SWE-bench Verified 81.9% · GPQA Diamond 81.5%)
- Hohe Generierungsgeschwindigkeit von 115 Token/Sekunde bei nur 850 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 | Kimi K2.5 | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $2.45 |
Output Tokens / Task Tokens generated per task | 24K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 8K | 36K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | Kimi K2.5 | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | kimi-k2-5 | models/gemini-4-argon |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2026-01 | 2026-09 |
| Knowledge Cutoff | November 2025 | June 2026 |
| Architecture | Multimodal Open Mixture-of-Experts Transformer | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | 1.2T total (36B active MoE) | Confidential |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Vertex AI Supervised Adapter Tuning & Context Distillation |
| 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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 744 GB VRAM (4-bit Quantized) · 2580 GB VRAM (Native BF16) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Google DeepMind Frontier Safety Framework Verified |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text, vision, audio, video |
| Output Modalities | text | text |
Kimi K2.5 vs Gemini 4 Argon: Schnelle Antworten (FAQ)
Which is more intelligent, Kimi K2.5 or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 86 on the composite Intelligence Index.
Which model is faster?
Gemini 4 Argon is faster, generating 115 tokens per second compared with 65 tokens per second.
Which model is cheaper to run?
Kimi K2.5 is cheaper at $0.00 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Kimi K2.5 has lower latency, with a time to first token of 0.62s compared with 0.85s.
Which model has a larger context window?
Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 262.1K tokens.
Fazit
Choose Kimi K2.5 for optimal für nativ multimodale verarbeitung von text und bild sowie visuelle dokumentenanalyse, enterprise-rag-systeme und skalierbare api-pipelines., or choose Gemini 4 Argon for optimal für nativ multimodale verarbeitung von text und bild sowie visuelle dokumentenanalyse, enterprise-rag-systeme und skalierbare api-pipelines..
Häufig gestellte Fragen (FAQ)
Which is cheaper: Kimi K2.5 or Gemini 4 Argon?+
Kimi K2.5 costs $0/1M input and $0/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Kimi K2.5 or Gemini 4 Argon?+
Kimi K2.5 supports 262K 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.
Related reading
- Aion-3.0 vs. Aion-Mini: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
- Aion-3.0 vs. Gemini 3.6 Flash: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
- Aion-Mini vs. Laguna XS 2.1: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
- Aya Expanse 32B vs. Llama 3.2 90B Vision: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
- Aya Expanse 8B vs. Command R+: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
- Aya Expanse 8B vs. Qwen 2.5 7B Instruct: Benchmarks, API-Preise & Geschwindigkeit im Vergleich (2026)Vergleich
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: Moonshot AI & Google Official Documentation