A purpose-built budget audio model that excels at voice tasks but stumbles on context length and general-purpose depth.
52
Coding
63
Writing
58
Research
0
Images
88
Value
28
Long Context
Use this when
Transcribing, analyzing, and responding to audio input cost-effectively without needing a separate speech-to-text pipeline.
Skip this if
You need long-context document analysis, image understanding, or top-tier reasoning performance, as the 32K window and 24B scale will bottleneck complex tasks.
Pricing
$0.10/1M in
$0.30/1M out
→0%since May 2026
Context
32k tokens
Speed
Fast
Voxtral Small is audio-in capable but does not support image input. The 32K context window is notably short for a 2025 model. Pricing is via Mistral's API; availability through third-party providers may vary. Check whether your use case requires audio input — the text-only version of Mistral Small 3.1 may be more appropriate for pure text workloads.
Native audio input support — understands spoken language directly without requiring external STT preprocessing
Extremely low cost at $0.10/$0.30 per 1M tokens, undercutting GPT-4o Audio and Gemini 1.5 Flash significantly
Solid multilingual audio handling, reflecting Mistral's strong European language coverage
Compact 24B size keeps inference fast despite multimodal capabilities
Weaknesses
32K context window is restrictive compared to competitors — Gemini 3.1 Pro offers 1M+ tokens, limiting long audio or document tasks
No image understanding despite the multimodal framing, narrowing its real-world versatility
Reasoning and complex coding benchmarks lag behind GPT-4o and Claude Sonnet 4.6 at their respective tiers
Real-world use cases
What people actually use Voxtral Small 24B 2507 for.
Transcribing and summarizing customer support call recordings in multiple languages
Building a budget voice assistant that processes spoken queries and returns structured text responses
Extracting action items from meeting audio files without a separate STT service
How Voxtral Small 24B 2507 compares
The nearest models people weigh against it, and what actually separates them.
vs Mistral Medium 3.1 — Against Mistral Medium 3.1 (Mistral), Voxtral Small 24B 2507 runs about 83% cheaper per token and gives up 4.1x on context. Take Voxtral Small 24B 2507 unless you specifically need what Mistral Medium 3.1 does better.
vs Llama 3.2 11B Vision Instruct — Against Llama 3.2 11B Vision Instruct (Meta), Voxtral Small 24B 2507 runs about 42% cheaper per token and gives up 4.1x on context. Take Voxtral Small 24B 2507 unless you specifically need what Llama 3.2 11B Vision Instruct does better.
vs Mistral Large 3 2512 — Against Mistral Large 3 2512 (Mistral), Voxtral Small 24B 2507 runs about 80% cheaper per token, gives up 8.2x on context and answers faster. Take Voxtral Small 24B 2507 unless you specifically need what Mistral Large 3 2512 does better.
Price History
Voxtral Small 24B 2507 pricing over time
→0% since May 31
90 data points · tracked daily since May 31, 2026
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Transcribing, analyzing, and responding to audio input cost-effectively without needing a separate speech-to-text pipeline.. Start free — no card required.
Mistral Medium 3.1 is a multimodal mid-tier model from Mistral that supersedes Mistral Large 2, offering vision capabilities alongside strong text performance at a significantly reduced price point. It targets the sweet spot between budget models and expensive flagships, with a 128K context window and competitive multilingual support.
Verdict
The best Mistral model for budget-conscious builders who still need multimodal capability and solid multilingual output.
Quality score
70%
Pricing
$0.40/1M in
$2.00/1M out
Speed
Fast
4/5 speed
Context
131k tokens
Officially supersedes Mistral Large 2, representing a generational shift in Mistral's lineup toward multimodal capability at lower cost tiers. Available via Mistral API and select cloud providers. No function calling limitations noted at this tier.
BudgetMultimodalMultilingualMid-tierVision
Best for
Cost-sensitive teams needing solid coding, instruction-following, and basic vision tasks without paying flagship prices.
Llama 3.2 11B Vision Instruct is Meta's open-weight multimodal model capable of understanding both text and images at an extremely low price point. It handles image captioning, visual question answering, and document analysis alongside standard text tasks.
Verdict
The go-to vision model when budget is the top constraint and good-enough accuracy is acceptable.
Quality score
57%
Pricing
$0.34/1M in
$0.34/1M out
Speed
Fast
4/5 speed
Context
131k tokens
Available via multiple inference providers including Together AI, Fireworks, and OpenRouter. As an open-weight model, it can also be self-hosted for even lower marginal costs at scale. Part of Meta's Llama 3.2 family which also includes a 90B vision variant for heavier workloads.
Open-weightVisionBudgetMultimodalMeta
Best for
Budget-conscious developers who need basic vision capabilities without paying premium multimodal prices.
Mistral Large 3 2512 is Mistral's flagship dense model updated in December 2025, offering strong multilingual reasoning and coding capabilities at a significantly reduced price point compared to its predecessor. It targets enterprise workloads that need high-quality outputs without paying top-tier frontier model prices.
Verdict
The best price-per-quality ratio in the non-mini flagship tier, especially for multilingual and long-context enterprise tasks.
Quality score
69%
Pricing
$0.50/1M in
$1.50/1M out
Speed
Balanced
3/5 speed
Context
262k tokens
Pricing of $0.50 input / $1.50 output per 1M tokens places it firmly in the budget-flagship category. Available via Mistral API (La Plateforme) and major cloud providers. December 2025 update ('2512') improves instruction following over the earlier 2407 release.
Multilingual enterprise tasks, code generation, and long-document analysis where cost efficiency matters more than absolute state-of-the-art performance.
Pricing moves, ranking shifts, and capability updates.
New ModelMar 27, 2026
Mistral: Voxtral Small 24B 2507 — added to UseRightAI
Mistral: Voxtral Small 24B 2507 (Mistral) is now indexed. It supersedes Mistral Small 3.1. A purpose-built budget audio model that excels at voice tasks but stumbles on context length and general-purpose depth.
Voxtral Small 24B 2507 costs $0.09999999999999999 per million input tokens and $0.3 per million output tokens on the API. A month of 10M input and 2M output tokens runs about $1.60 at list price, before any batch or caching discounts.
What is Voxtral Small 24B 2507 best for?
Voxtral Small 24B 2507 is best for transcribing, analyzing, and responding to audio input cost-effectively without needing a separate speech-to-text pipeline.. It is a strong fit when that workflow matters more than the tradeoffs around budget pricing and fast speed.
When should I avoid Voxtral Small 24B 2507?
You need long-context document analysis, image understanding, or top-tier reasoning performance, as the 32K window and 24B scale will bottleneck complex tasks.
What is a cheaper alternative to Voxtral Small 24B 2507?
Mistral Small 3.1 (Mistral) at $0.10/1M/1M input against Voxtral Small 24B 2507's $0.10/1M/1M. Ultra-cheap multimodal model for massive-volume, low-complexity pipelines. Compare it first if Voxtral Small 24B 2507's pricing is the thing stopping you.
What is a faster alternative to Voxtral Small 24B 2507?
Mistral Medium 3.1 — fast against Voxtral Small 24B 2507's fast, with 131k tokens of context. Worth the swap when response time is what your users notice rather than the last few points of reasoning depth.
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