🤖 LLM Model Selection Guide (2025 Q2)

Only pay for the power you need—this guide distills public benchmarks, vendor docs, and independent evaluations so you can juggle price, speed, context window, multimodality, tool-use, and raw reasoning in seconds.

🗺️ Quick-pick matrix

Need this most	Pick this model	Why
⚡ Sub-300 ms, < $0.001	Gemini 2.0 Flash Lite · Claude 3 Haiku	First-token latency ≈ 0.25 s; rock-bottom cost
🧠 Elite reasoning (budget ≠ issue)	Claude 3 Opus / 3.7 Sonnet IR	Top scores on MMLU, GPQA, HumanEval
📚 ≥ 1 M-token context	Gemini 1.5 Flash / Pro	Up to 2 M tokens (1 M input + 1 M output); multimodal
🖥️ Self-host / on-prem	Llama 3 70B Instruct	Open weights, GPT-3.5-grade accuracy
🔍 Turn-key RAG	Cohere Command R / R-Plus	Built-in retrieval & function-calling; 128 K context
💵 AWS-native, 32 K context	Amazon Nova Pro	Runs inside Bedrock; IAM, VPC, KMS integration
🐍 Bilingual EN-ZH coding	DeepSeek R1-V1	90 % MMLU, 97 % MATH500; open weights

(Prices = input-token unless noted. See the pricing sheet.)

Google · Gemini family

Gemini 2.0 Flash Lite • 1 M ctx

Multimodal: text + image + audio in, text out

Tool use: Native function-calling (JSON schema)

Benchmarks: MMLU-Pro 71.6 %; HiddenMath 55 %

Strengths: fastest Gemini, cheapest; watermarking on media

Caveats: shallower reasoning vs Flash/Pro

Gemini 2.0 Flash • 1 M ctx

2× speed of 1.5 Pro; adds multimodal output (image & speech)

MMLU-Pro 77.6 %; HiddenMath 63.5 %

Parallel search/tool calls improve accuracy & grounding

Gemini 1.5 Flash 8B / Flash • 1.5 M ctx

Distilled from 1.5 Pro; sweet-spot cost/quality

MMLU-Pro 67 %; good for RAG over giant PDFs & video

Gemini 1.5 Pro • 2 M ctx

First model past 90 % MMLU; excels in code & long-form reasoning

Closed beta; pricey & slower

Anthropic · Claude 3 / 3.5 / 3.7 line

Model	Context	Benchmarks*	Multimodal	Tool use	Notes
Haiku	200 K	55 % MMLU	Text	⚙︎ (prompt-level)	Sub-sec, budget tier
Sonnet 3.5	200 K	78 % MMLU, 64 % HumanEval	Vision SOTA (June 24)	⚙︎	Mid-tier; beats 3 Opus
Sonnet 3.7 IR	200 K (+128 K CoT)	≈ GPT-4 on hard maths	Vision	⚙︎ + Integrated reasoning switch	Fast or reflective modes
Opus 3.0	100 K	80 % MMLU	Vision	⚙︎	Deep reasoning, higher latency

*percentages are representative single-shot scores.

Alignment & safety

All Claude 3 models use Constitutional AI; 3.7 adds “Constitutional Classifiers” → toughest to jailbreak among majors.

DeepSeek · R1-V1 (open)

Arch: 37 B active · 671 B MoE, 128 K ctx

Benchmarks: 90.8 % MMLU, 97 % MATH500, Codeforces 96th pct

Multimodal: Text-only

Tool use: No native JSON schema, but excels with ReAct prompts

Safety: minimal RLHF → add your own moderation

Meta · Llama 3 & 4

Llama 3 Instruct

Size	Context	MMLU	HumanEval	Best for
1 B / 8 B	128 K	60 %	20-30 %	Edge, CPU, tagging
70 B	128 K	85 %	50 %	Self-hosted RAG, privacy

Llama 4 Scout 17 B • 10 M ctx

Multimodal text + image, 12 langs

Benchmarks: Outperforms Llama 3 70B; SOTA ≤20 B class

Tool use: Designed for agents; Bedrock/Azure endpoints

Llama 4 Maverick 17 B • 1 M ctx

128-expert MoE, higher accuracy vs Scout, slightly slower

Aims at GPT-4 class while 3-4× cheaper

Cohere · Command family

Model	Params	Context	Benchmarks	Tool use	Niche
Light	~ ? B	4 K	55 % MMLU	Prompt-level	Cheap chat, routing
R	35 B	128 K	75 % MMLU, top Arena (’24)	Native func-call, cites sources	Long-doc RAG
R Plus	104 B	128 K	≈ GPT-4 on RAG, 2× faster	Multi-step, self-correct	Frontier RAG & agents

Amazon · Nova series

Model	Context	Multimodal	Benchmarks (≈)	Fine-tune	Ideal
Micro	128 K	❌	Beats GPT-4o-mini by ≈2 %	✔︎ text	Low-latency bulk chat
Lite	300 K	✔︎ (image + video)	Strong vis-text, < Nova Pro	✔︎ text+vision	Doc & media analysis
Pro	300 K	✔︎✔︎	Near GPT-4o on RAG, 2× faster, 65 % cheaper	✔︎	Enterprise, multilingual, agents

🛠️ Cost-saving tips

Prototype on Flash Lite / Haiku then upgrade only where accuracy gaps appear.

Keep sessions alive try to use sessions with your ingested data instead of creating new ones and ingesting the same.

Don't repeat ingestion don't forget to skip ingestion if you already did it t avoid re-processing and more token usage.

Optimize your data the less the best.

Sources: Google DeepMind, Anthropic (Claude 3.7 IR white-paper), DeepSeek-AI, Meta AI (Llama 3 & 4 model cards), Cohere tech blog, AWS re:Invent 2024 Nova launch, FloTorch CRAG benchmark.

Supported Models

🤖 LLM Model Selection Guide (2025 Q2)#

🗺️ Quick-pick matrix#

Google · Gemini family#

Gemini 2.0 Flash Lite • 1 M ctx#

Gemini 2.0 Flash • 1 M ctx#

Gemini 1.5 Flash 8B / Flash • 1.5 M ctx#

Gemini 1.5 Pro • 2 M ctx#

Anthropic · Claude 3 / 3.5 / 3.7 line#

Alignment & safety#

DeepSeek · R1-V1 (open)#

Meta · Llama 3 & 4#

Llama 3 Instruct#

Llama 4 Scout 17 B • 10 M ctx#

Llama 4 Maverick 17 B • 1 M ctx#

Cohere · Command family#

Amazon · Nova series#

🛠️ Cost-saving tips#