Head-to-Head Model Comparison

Gemma 4 31B vs Mistral Small 4

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

Ganti & Bandingkan Model Apa Saja

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Gemma 4 31BProfile

Mistral Small 4Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Google

Gemma 4 31B

Penalaran Bobot Terbuka

Gemma 4 31B dari Google adalah model AI yang dirancang untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, menghadirkan jendela konteks 262K, kecepatan 80 token/detik, indeks kecerdasan 81/100, serta ak

Intelligence Index81 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

68.5%

Mistral AI

Mistral Small 4

Inferensi Super Cepat

Mistral Small 4 dari Mistral AI adalah model AI yang dirancang untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, menghadirkan jendela konteks 256K, kecepatan 120 token/detik, indeks kecerdasan 48/100, sert

Intelligence Index48 / 100

Input Token:

$0.10 / 1M

Output Token:

$0.30 / 1M

Context Window:

256K tokens

SWE-bench Verified:

44.5%

Perbedaan Utama Sekilas

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%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	Gemma 4 31B	Mistral Small 4
Intelligence Index Composite reasoning & knowledge score (0-100)	81	48
Provider	Google	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

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Gemma 4 31B

+24.0% SWE-bench lead

Cost

Gemma 4 31B

1.0x cheaper monthly runtime

Speed

Mistral Small 4

+50% faster generation

Context

Gemma 4 31B

262.1K tokens window

Skor Benchmark Terverifikasi

Official evaluation metrics verified by independent third-party research labs.

Gemma 4 31BMistral Small 4

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

68.5%vs44.5%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

77%vs56%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

84.8%vs78%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1410ptsvs1315pts

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

N/Avs38%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

86%vs66%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

N/Avs80%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

80 tok/s vs120 tok/s

Latensi & Waktu Respons

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

Gemma 4 31B0.48s

Mistral Small 40.34s

Est. End-to-End Response (500 tokens)TTFT + generation time

Gemma 4 31B6.73s

Mistral Small 44.51s

Simulator Biaya Token Interaktif

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Gemma 4 31B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Mistral Small 4 Monthly API Bill

$0.47 / month

$0.1/1M in · $0.3/1M out

Gemma 4 31B saves approximately $0.47 / month (100%) under this workload.1.0x Savings

Gemma 4 31B · Strengths & Key Advantages

Jendela konteks 262K dengan kapasitas output maksimum hingga 33K token (SWE-bench Verified 68.5% · GPQA Diamond 77%)
Kecepatan generasi tinggi 80 token/detik dengan latensi token pertama (TTFT) hanya 480 ms
Bobot terbuka (31B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Mistral Small 4 · Strengths & Key Advantages

Jendela konteks 256K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 44.5% · GPQA Diamond 56%)
Kecepatan generasi tinggi 120 token/detik dengan latensi token pertama (TTFT) hanya 340 ms
Bobot terbuka (24B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

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

Spesifikasi Teknis

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: Jawaban Cepat (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.

Kesimpulan

Baik Gemma 4 31B (Sangat cocok untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Mistral Small 4 (Sangat cocok untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Gemma 4 31B unggul dalam penalaran dan pemrograman, Mistral Small 4 memberikan kecepatan generasi token lebih tinggi, dan Gemma 4 31B lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Gemma 4 31B atau Mistral Small 4?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Gemma 4 31B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Gemma 4 31B vs Mistral Small 4?+

Gemma 4 31B mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan Mistral Small 4 sebesar $0.10 / $0.30. Secara keseluruhan, Gemma 4 31B lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Gemma 4 31B mendukung konteks 262.1K token dibandingkan 256K token pada Mistral Small 4, sementara Mistral Small 4 unggul dalam kecepatan output token per detik.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Gemma 4 31B vs Mistral Small 4

Ganti & Bandingkan Model Apa Saja

Gemma 4 31B

Mistral Small 4

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Gemma 4 31B · Strengths & Key Advantages

Considerations & Trade-offs

Mistral Small 4 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Gemma 4 31B vs Mistral Small 4: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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