1stTop pick
Researched 34d agoLongCat-2.0
- MMLU-Pro
- —
- Output (from)
- $2.95 / 1M
Best Small Language Models Under 10B Parameters (2026)
Last refreshed 2026-07-13. Next refresh: weekly.
The best small LLMs under 10B parameters in 2026 — fast, cheap, and deployable on-device or at the edge with strong benchmark scores.
Verdict
Use LongCat-2.0 for small-model deployments today.
T5Gemma is the runner-up: — vs — on MMLU-Pro.
2ndShortlist
Researched 41d agoT5Gemma
- MMLU-Pro
- —
- Output (from)
- Free
Single-source resultMiniMax M2.7 scored 80.4% on MMLU-Pro, more than five points above the next GA score (56.0%). We dropped it one GA rank until another source corroborates the result.
How we rank
Small models (≤10B active parameters) rank on MMLU-Pro, then GPQA Diamond, MMLU, and HellaSwag.
- Eligibility — Non-deprecated models with ≤10B parameters (billions-only parser).
- Primary ranking — MMLU-Pro, then GPQA Diamond, then MMLU, then HellaSwag, then newer release.
- Podium freshness — Shortlist cards require `lastResearched` within 60 days and a tracked public output price. Stale or unpriced SKUs stay in the table with a “Verify pricing” badge once research is past 45 days.
- Variant collapse — We keep one row per model family (`familySlug` + parameter tier). When headline scores tie within ±0.5 pt (±10 Elo on Chatbot Arena), we pick the canonical SKU by lowest tracked input price, then GA over preview or limited access, then newest `release`. A folded sibling within the benchmark noise band can show a "Tied within margin" chip on that score cell.
- Pricing — SLMs often win on unit economics — compare the provider ladder before picking.
| # | Model | Input $/1M | Output $/1M | |
|---|---|---|---|---|
| 1 | Granite 4.1 8B MMLU-Pro: 55.99% | $0.05 | $0.10 | |
| 2 | MiniMax M2.7 ReasoningTools MMLU-Pro: 80.43% | $0.28 | $1.20 | |
| 3 | Phi-4 Mini MMLU-Pro: 52.8% | $0.90 | $0.90 | |
| 4 | Gemma 2 9B MMLU-Pro: 52.08% | $0.06 | $0.18 | |
| 5 | LFM2.5 8B A1B ReasoningTools MMLU-Pro: 50.5% | — | — | |
| 6 | Granite 4.1 3B MMLU-Pro: 49.83% | — | — | |
| 7 | Phi-3 Mini 4k MMLU-Pro: 45.66% | $0.05 | $0.25 | |
| 8 | LFM2.5 1.2B Instruct Tools MMLU-Pro: 44.35% | — | — | |
| 9 | Llama 3.1 8B Instruct MMLU-Pro: 44.25% | $0.02 | $0.05 | |
| 10 | Llama 3 8B Instruct MMLU-Pro: 40.5% | $0.02 | $0.04 | |
| 11 | Llama 3.2 3B Instruct MMLU-Pro: 34.7% | $0.03 | $0.05 | |
| 12 | Llama 3.2 1B Instruct MMLU-Pro: 20% | $0.03 | $0.10 | |
| 13 | Qwen3-8B MMLU-Pro: — | $0.04 | $0.14 | |
| 14 | Qwen2-7B MMLU-Pro: — | $0.05 | $0.15 | |
| 15 | Gemma 7B Instruct MMLU-Pro: — | $0.05 | $0.07 | |
| 16 | OpenChat 3.5 (0106) MMLU-Pro: — | $0.07 | $0.07 | |
| 17 | Starling LM 7B Beta MMLU-Pro: — | — | — | |
| 18 | Zephyr 7B Beta MMLU-Pro: — | $0.05 | $0.20 | |
| 19 | Qwen2.5-7B-Instruct MMLU-Pro: — | $0.03 | $0.03 | |
| 20 | Aya 23 8B MMLU-Pro: — | — | — |
Honorable mentions
Next seats in this ranking. Lines below are from each model's stored description in LLMReference seed data—spot-check the model page before relying on a capability claim.
Mistral Voxtral Mini 3B 2507 is MistralAI's Voxtral model. It was released 2025-07-01.
—
MMLU-Pro
IBM Granite Vision 3.3 2B improves on Granite Vision 3.2 with a new SigLIP2 vision encoder, higher-quality training data, and experimental capabilities: image segmentation, doctags generation, and multi-page support (up to 8 pages). Enhanced safety compared to 3.2. Architecture: SigLIP2 + 2-layer MLP (GELU) + Granite 3.1 2B Instruct. Benchmarks: DocVQA 0.91, TextVQA 0.80, OCRBench 0.79, InfoVQA 0.68. Apache 2.0.
—
MMLU-Pro
Qwen3 Reranker 8B is Alibaba's multilingual reranking model from the Qwen3 generation, designed for retrieval-augmented generation pipelines. Open-sourced under Apache 2.0. Achieves 81.22 on MTEB-Code and 72.94 on MMTEB-R. Released alongside Qwen3 Embedding series June 2025.
—
MMLU-Pro
Compare Top Picks
Side-by-side comparison of the top picks by price, benchmark, and API access.
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Frequently asked questions
Which LLM is best for small-model deployment?
LongCat-2.0 is the current LLMReference top pick for small-model deployment. The verdict uses the stored category signal MMLU-Pro: —. Output pricing starts at $2.95 per 1M tokens. Review the linked model and provider pages before production use because availability and pricing can change.
How does LongCat-2.0 compare to T5Gemma for small-model deployment?
LongCat-2.0 leads T5Gemma in the visible shortlist on MMLU-Pro: — versus —. The pricing cards show LongCat-2.0: output pricing starts at $2.95 per 1m tokens and T5Gemma: output pricing is listed as free.
How does LLMReference rank LLMs for small-model deployment?
LLMReference ranks LLMs for small-model deployment from stored model, benchmark, freshness, and pricing data. The current methodology summary is: Small models (≤10B active parameters) rank on MMLU-Pro, then GPQA Diamond, MMLU, and HellaSwag.
How often is this list updated?
The LLM rankings on this page are updated daily as new benchmark scores, provider availability, and pricing data are tracked. The "as of" date at the top of the page shows the most recent refresh.
How do you decide which models appear in the top 3?
The podium picks are driven by the primary benchmark signal for this category (shown in the Methodology section), filtered to non-deprecated models with confirmed API availability. In ties, we prefer the more recently released model.
Are preview or beta models included?
Preview models appear in the "Watch list" section but are not in the main ranked podium unless the category explicitly allows it (e.g., /best/coding and /best/agents, where preview models often lead benchmarks).
Can I compare two specific models head-to-head?
Yes — use the Compare tool at llmreference.com/compare for a side-by-side breakdown of context window, pricing, benchmarks, and provider availability.
Is the pricing data real-time?
Pricing is tracked from provider documentation and updated regularly. It reflects the best available public data, not live API quotes — always verify before billing.