Gemma 4 31B vs Mistral Small 4
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Gemma 4 31B by Google and Mistral Small 4 by Mistral AI 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, Gemma 4 31B holds a notable pricing advantage for token ingestion. On output generation, Gemma 4 31B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Gemma 4 31B provides an expanded memory horizon with 262K tokens. In latency-sensitive production environments, Mistral Small 4 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.
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Gemma 4 31B
Open-Weights-ModellGemma 4 31B von Google ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 262K Token zu API-Preisen von $0/1M Input- und $0/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.00 / 1M
$0.00 / 1M
262.1K tokens
68.5%
Mistral Small 4
Open-Weights-ModellMistral Small 4 von Mistral AI ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 256K Token zu API-Preisen von $0.15/1M Input- und $0.6/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.10 / 1M
$0.30 / 1M
256K tokens
44.5%
Wichtigste Unterschiede auf einen Blick
- Gemma 4 31B is 1.0x cheaper for API usage (input + output tokens combined).
- Gemma 4 31B has a larger context window (262K vs 256K tokens).
- Mistral Small 4 is faster at 120 tok/s vs 80 tok/s.
- Gemma 4 31B leads on SWE-bench coding (68.5% vs 44.5%).
| Feature | Gemma 4 31B | Mistral Small 4 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 81 | 48 |
Provider | Mistral AI | |
Input Price Cost per million input tokens | $0.00 / 1M | $0.10 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.30 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $0.07 / 1M |
Context Window Maximum input + output tokens | 262.1K tokens~393 A4 pages of text | 256K tokens~384 A4 pages of text |
Max Output Tokens Per single request | 33K | 16K |
Throughput Speed Tokens generated per second | 80 tok/s | 120 tok/s |
SWE-bench Verified Real-world software engineering | 68.5% | 44.5% |
GPQA Diamond Graduate-level reasoning | 77% | 56% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.03 / 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.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
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.
Gemma 4 31B · Strengths & Key Advantages
- Fortschrittliche Dense open reasoning transformer with native thinking-Architektur von Google mit 262K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0/1M Input, $0/1M 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
Mistral Small 4 · Strengths & Key Advantages
- Fortschrittliche Next-Gen Compact Multimodal Transformer-Architektur von Mistral AI mit 256K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0.15/1M Input, $0.6/1M 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 | Gemma 4 31B | Mistral Small 4 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.12 |
Output Tokens / Task Tokens generated per task | 12K | 28K |
Reasoning Tokens / Task Thinking tokens burned per task | 6K | 12K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemma 4 31B | Mistral Small 4 |
|---|---|---|
| Official API Model ID (SDK) | models/gemma-4-31b | mistral-small-4-latest |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-04 | 2026-03 |
| Knowledge Cutoff | January 2026 | January 2026 |
| Architecture | Dense open reasoning transformer with native thinking | Next-Gen Compact Multimodal Transformer |
| Parameters | 31B | 24B |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Flat rate ($0.1/1M in) across full 256K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Cached input at $0.03/1M tokens |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| 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 | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 19 GB VRAM (4-bit Quantized) · 67 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 15 GB VRAM (4-bit Quantized) · 52 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Google DeepMind Frontier Safety Framework Verified | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
Gemma 4 31B vs Mistral Small 4: Schnelle Antworten (FAQ)
Which is more intelligent, Gemma 4 31B or Mistral Small 4?
Gemma 4 31B is more intelligent. It scores 81 compared with 48 on the composite Intelligence Index.
Which model is faster?
Mistral Small 4 is faster, generating 120 tokens per second compared with 80 tokens per second.
Which model is cheaper to run?
Gemma 4 31B is cheaper at $0.00 per 1M tokens blended, compared with $0.07 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Mistral Small 4 has lower latency, with a time to first token of 0.34s compared with 0.48s.
Which model has a larger context window?
Gemma 4 31B has a larger context window, supporting 262.1K tokens compared with 256K tokens.
Fazit
Verdict & Model Selection Recommendation: Choose Gemma 4 31B if your priority is Private enterprise reasoning servers, local software development, and deep research. Its performance in demanding tasks justifies its operational token cost. Choose Mistral Small 4 if your workflow prioritizes High-volume agent orchestration, low-cost document parsing, and edge deployments. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Häufig gestellte Fragen (FAQ)
How does Gemma 4 31B compare to Mistral Small 4 in API token pricing and cost per token?+
Gemma 4 31B costs $0.00 per 1M input tokens and $0.00 per 1M output tokens, whereas Mistral Small 4 costs $0.10 input and $0.30 output. Overall, Gemma 4 31B 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: Gemma 4 31B or Mistral Small 4?+
Gemma 4 31B supports a context window of 262K tokens (~197k words), while Mistral Small 4 supports 256K tokens (~192k words). Therefore, Gemma 4 31B can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Gemma 4 31B or Mistral Small 4?+
Gemma 4 31B scores 68.5% on SWE-bench Verified while Mistral Small 4 scores 44.5%. For autonomous software engineering and multi-file debugging, Gemma 4 31B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Gemma 4 31B and Mistral Small 4?+
Gemma 4 31B features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Mistral Small 4 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Gemma 4 31B and Mistral Small 4?+
Gemma 4 31B generates approximately 80 tokens per second, while Mistral Small 4 delivers 120 tokens per second. In latency-sensitive production environments, Mistral Small 4 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Gemma 4 31B and Mistral Small 4 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Gemma 4 31B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Mistral Small 4. 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.
Related reading
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- Gemma 4 31B vs Llama 3.3 70B: Benchmarks, Pricing & Speed ComparisonVergleich
- Gemma 4 E2B vs Gemma 4 31B: Benchmarks, Pricing & Speed ComparisonVergleich
- Mistral Small 4 vs Claude Haiku 5.5: Benchmarks, Pricing & Speed ComparisonVergleich
- Mistral Small 4 vs GPT-4.1 mini: Benchmarks, Pricing & Speed ComparisonVergleich
- Nemotron 3 Nano Omni 30B A3B Reasoning vs Mistral Small 4: Benchmarks, Pricing & Speed ComparisonVergleich
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: GetInfoUs Independent AI Benchmarks & Official API Specs