Top 10 Weekly LLM Models: Complete Reviews

Complete reviews of the top 10 LLM models by weekly token consumption. Based on OpenRouter data, community feedback, and pricing analysis.

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#1: GPT-5.6 Luna (OpenAI)

Weekly Tokens: 16.1T | Rank: #1

Spec	Value
Context	1,050,000 tokens
Input	$0.20/M tokens
Output	$1.20/M tokens
Modality	Text + Image + File
Created	July 9, 2026

Strengths

• 1M context window — handle entire codebases in one prompt
• Fast inference — optimized for low-latency applications
• Multimodal — text, image, and file inputs
• Cheapest in GPT-6 family — 50x cheaper than GPT-6 Astra
• Good enough quality — capable reasoning for most tasks

Weaknesses

• Not the best at reasoning — GPT-6 Astra significantly better for complex logic
• Limited agentic capabilities — not designed for complex multi-step workflows
• Newer model — less community testing and tooling support

Best Use Cases

• High-volume chat applications
• Classification and extraction tasks
• Lightweight agentic workflows
• Content generation at scale
• Cost-sensitive applications

Verdict

The volume king. Luna proves that for most tasks, good enough quality at a fraction of the cost wins. It’s #1 because 90% of AI tasks don’t need frontier reasoning — they need fast, cheap, reliable responses.

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#2: DeepSeek V4.1 Flash (DeepSeek)

Weekly Tokens: 12.5T | Rank: #2

Spec	Value
Context	1,048,576 tokens
Input	$0.15/M tokens
Output	$0.60/M tokens
Modality	Text + Image
Created	September 10, 2026

Strengths

• Best value for coding — rivals GPT-6 on many coding tasks
• CED architecture — first Causal Encoder-Decoder from DeepSeek
• Asymmetric MoE — 8B active on input, 16B on output (552B total)
• Compressed KV caching — 75% less cache memory than V3
• Time-based pricing — cheaper off-peak, free weekends

Weaknesses

• Limited modality — text + image only (no file input)
• Newer model — less community tooling
• Time-based pricing confusion — costs vary by time of day

Best Use Cases

• Coding agents and IDE integrations
• Terminal and computer-use agents
• Long-horizon multi-step tasks
• Cost-sensitive coding workflows
• High-volume batch processing

Verdict

The value champion. DeepSeek V4.1 Flash proves you don’t need to spend OpenAI prices for great coding results. The CED architecture and compressed KV caching make it ideal for agentic workloads.

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#3: Tencent Hy4 Preview (Tencent)

Weekly Tokens: 12.4T | Rank: #3

Spec	Value
Context	1,048,576 tokens
Input	$0.834/M tokens
Output	$2.501/M tokens
Modality	Text
Created	August 28, 2026

Strengths

• Strong on agentic workflows — designed for complex tool-use
• 49B active / 770B total MoE — large model, efficient inference
• 1M context — suitable for long documents
• Multi-step execution — built for sustained workflows

Weaknesses

• Preview status — may have bugs and instability
• Text only — no image or file input
• Limited availability — newer model, less widespread adoption

Best Use Cases

• Complex coding agents
• Multi-step tool-use workflows
• Productivity applications
• Automation tasks

Verdict

The agentic contender. Tencent’s Hy4 is purpose-built for agentic workflows. It’s impressive that a Chinese model is competing directly with OpenAI and DeepSeek on complex tasks.

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#4: Z.ai GLM 5.3 Flash (Z.ai)

Weekly Tokens: 11.9T | Rank: #4

Spec	Value
Context	1,310,720 tokens
Input	$0.075/M tokens
Output	$0.25/M tokens
Modality	Text
Created	August 26, 2026

Strengths

• Largest context in top 10 — 1.31M tokens
• Hybrid attention — sparse + linear for efficient long-context
• 133x cheaper than GPT-6 — $0.075 vs $10 input
• 50% discount on OpenRouter — even cheaper currently
• Strong in Academia — ranked #1

Weaknesses

• Text only — no image or file input
• Less known — smaller community and tooling ecosystem
• Newer model — less battle-tested

Best Use Cases

• Long document analysis
• Academic research
• Legal document review
• Any task requiring maximum context

Verdict

The context king. GLM 5.3 Flash has the largest context window in the top 10 and is the cheapest per token. For tasks that need to process entire documents, it’s the clear choice.

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#5: DeepSeek V4 Flash 0731 (DeepSeek)

Weekly Tokens: 10.9T | Rank: #5

Spec	Value
Context	1,310,720 tokens
Input	$0.056/M tokens
Output	$0.177/M tokens
Modality	Text
Created	July 31, 2026

Strengths

• Category leader — #1 in Academia, Finance, Health, Marketing
• 1.31M context — largest context in top 10
• Cheapest in top 5 — $0.056 input
• GA release — stable, production-ready
• 13B active / 284B total MoE — efficient architecture

Weaknesses

• Text only — no image or file input
• Older than V4.1 — superseded by newer model

Best Use Cases

• Academic research
• Financial analysis
• Health/medical text processing
• Marketing content generation
• Category-specific applications

Verdict

The specialist. DeepSeek V4 Flash 0731 dominates specific categories. If you’re in academia, finance, health, or marketing, this is the model to use.

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#6: Xiaomi MiMo-V2.5 (Xiaomi)

Weekly Tokens: 7.77T | Rank: #6

Spec	Value
Context	1,048,576 tokens
Input	$0.119/M tokens
Output	$0.238/M tokens
Modality	Text + Image + Audio
Created	April 22, 2026

Strengths

• Omnimodal — text, image, AND audio input
• Pro-level agentic performance at half the cost
• 1M context — suitable for long documents
• 15% discount on OpenRouter — even cheaper currently

Weaknesses

• Less known — smaller community
• Newer model — less battle-tested for production

Best Use Cases

• Multimodal applications (audio + image + text)
• Agentic coding workflows
• Video understanding tasks
• Cost-sensitive multimodal projects

Verdict

The multimodal dark horse. Xiaomi’s MiMo-V2.5 is the only omnimodal model in the top 10. For applications that need audio understanding, it’s the clear choice.

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#7: Tencent Hy3 (Tencent)

Weekly Tokens: 5.05T | Rank: #7

Spec	Value
Context	256,000 tokens
Input	25% off on OpenRouter
Output	25% off on OpenRouter
Modality	Text
Created	August 28, 2026

Strengths

• Configurable reasoning — no-think, low-thought, high-thought modes
• 295B MoE with 21B active — large but efficient
• Agentic workflows — built for real-world production
• 25% discount on OpenRouter — good value

Weaknesses

• Smaller context — only 256K (others have 1M+)
• Newer model — less community support

Best Use Cases

• Reasoning-heavy tasks
• Agentic coding workflows
• Math and logic problems
• Configurable reasoning needs

Verdict

The reasoning specialist. Hy3’s configurable reasoning modes let you trade speed for quality as needed. Good for tasks where you sometimes need deep thinking and sometimes need fast responses.

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#8: DeepSeek V4 Flash Vision (DeepSeek)

Weekly Tokens: 4.8T | Rank: #8

Spec	Value
Context	1,048,576 tokens
Input	$0.05/M tokens
Output	$0.15/M tokens
Modality	Text + Image
Created	July 31, 2026

Strengths

• 200x cheaper than GPT-6 — $0.05 vs $10 input
• Vision capable — text + image input
• 1M context — suitable for long documents
• Vision + coding — good for screenshot-to-code workflows

Weaknesses

• Experimental — may have bugs and limitations
• Vision is experimental — not as capable as dedicated vision models

Best Use Cases

• Screenshot-to-code workflows
• Image understanding in coding contexts
• Cost-sensitive vision tasks
• Document analysis with images

Verdict

The budget vision model. If you need image understanding but can’t afford GPT-6 prices, this is your best bet.

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#9: Gemini 3.8 Flash (Google)

Weekly Tokens: 4.5T | Rank: #9

Spec	Value
Context	1,048,576 tokens
Input	$0.75/M tokens
Output	$3.75/M tokens
Modality	Text + Image + Audio
Created	August 30, 2026

Strengths

• Native multimodal — text, image, AND audio
• Google ecosystem — integrates with Google services
• 1M context — suitable for long documents
• Built-in web search — no extra tool needed

Weaknesses

• More expensive — $0.75 input vs $0.05-0.15 for cheaper models
• Google dependency — tied to Google infrastructure

Best Use Cases

• Multimodal applications
• Google ecosystem integration
• Tasks requiring web search
• Long document analysis

Verdict

The Google play. Gemini 3.8 Flash is Google’s fastest model and a solid choice if you’re already in the Google ecosystem.

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#10: GPT-6 Astra (OpenAI)

Weekly Tokens: 4.2T | Rank: #10

Spec	Value
Context	1,050,000 tokens
Input	$10.00/M tokens
Output	$50.00/M tokens
Modality	Text + Image + File
Created	September 4, 2026

Strengths

• Best reasoning quality — OpenAI’s flagship
• Mandatory reasoning — always thinks before responding
• 5 effort levels — max, xhigh, high, medium, low
• 1M context — handle large inputs
• Multimodal — text, image, file inputs

Weaknesses

• Most expensive — $10 input, $50 output
• Slowest — mandatory reasoning adds latency
• Overkill for simple tasks — most tasks don’t need this quality

Best Use Cases

• Final output generation
• Client-facing content
• Complex reasoning tasks
• Safety-critical applications
• When cost is no object

Verdict

The premium choice. GPT-6 Astra is the best model available — but it’s 50-200x more expensive than alternatives. Use it only when you absolutely need the best.

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Summary: Which Model for Which Task?

Task	Best Model	Why
High-volume chat	GPT-5.6 Luna	Cheapest, fast, 1M context
Coding agent	DeepSeek V4.1 Flash	Best value for coding
Long documents	Z.ai GLM 5.3 Flash	1.31M context, cheapest
Academia/Finance	DeepSeek V4 Flash 0731	Category leader
Multimodal	Xiaomi MiMo-V2.5	Only omnimodal in top 10
Reasoning	Tencent Hy3	Configurable reasoning
Vision	DeepSeek V4 Flash Vision	Cheapest with vision
Google ecosystem	Gemini 3.8 Flash	Native Google integration
Best quality	Claude Fable 5.1	Highest intelligence index (53.4)
Agentic workflows	GLM 5.3 Flash	Agentic 51.2 at 1/111th GPT-6 price

Intelligence Index Rankings (Artificial Analysis)

How the top 10 stack up on measured intelligence:

Rank	Model	Intelligence	Coding	Agentic
1	Claude Fable 5.1	53.4	—	—
2	GPT-6 Astra	52.8	76.9	51.5
3	GLM 5.3 Flash	41.9	71.5	51.2
4	Gemini 3.8 Flash	41.2	76.3	41.1
5	DeepSeek V4.1 Flash	39.5	—	—
6	GPT-5.6 Luna	37.5	71.4	42.7
7	DeepSeek V4 Flash 0731	34.5	69.1	41.7
8	Xiaomi MiMo-V2.5	22.3	56.8	17.4

Note: Tencent Hy4, Tencent Hy3, DeepSeek V4 Flash Vision, Gemini and others without indices haven’t been scored by Artificial Analysis yet. Absence of data ≠ absence of capability.

Reasoning Effort Defaults

Each model ships with different default thinking behavior — this affects both latency and quality:

Model	Reasoning	Default Effort	Supported Efforts
GLM 5.3 Flash	Mandatory	MAX	max, high, low
GPT-6 Astra	Mandatory	medium	max, xhigh, high, medium, low
Gemini 3.8 Flash	Mandatory	medium	high, medium, low
Muse Spark 1.3	Mandatory	medium	max, xhigh, high, medium, low, minimal
Sakana Fugu Ultra	Mandatory	xhigh	max, xhigh, high
DeepSeek V4.1 Flash	Optional	high	max, high, low
GPT-5.6 Luna	Optional	medium	max, xhigh, high, medium, low, none
Tencent Hy4	Optional	high	high, low, none
Mercury 2.5	Optional	medium	high, medium, low, none

Why this matters: GLM 5.3 Flash defaults to MAX effort — it thinks hardest by default, explaining its outsized agentic index. GPT-6 Astra defaults to medium. If you’re comparing latency, remember you’re not comparing equal thinking time.

Cost lever: On models with optional reasoning (Luna, DeepSeek, Hy4), setting effort to none trades quality for maximum speed and minimum cost.

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Data: OpenRouter API, October 2025.