Best LLM for Image Understanding in 2026

For image understanding, GPT-6 Luna is our pick: $0.18/M tokens on a Image analysis call workload, 1.1M context.

Vision tasks — reading a screenshot, describing a photo, parsing a chart — need a model that accepts image input at all, which is a hard requirement, not a nice-to-have. Among vision-capable models we rank on context window and price.

Verdict: We have not run a controlled quality test for this task — ranking here is a requirements match among vision-capable models on context window and price, not a visual-accuracy comparison.

Quick answer: What is the best LLM for image understanding?

GPT-6 Luna, from OpenAI, is the best fit for image understanding at $0.18 per million task tokens on a Image analysis call workload, with a 1.1M-token context window. No cheaper value pick beats it for this task.

Verified 2026-08-08

Best overall

GPT-6 Luna

OpenAI · $0.18/M

Fit 82/100 — the top requirements match for this task.

Best value

GPT-6 Luna

OpenAI · $0.18/M

The strongest fit among budget and mid-tier priced models.

Fastest

Qwen 3.8 30B

Groq · $1.11/M

690 tokens/sec measured.

Longest context

Gemini 3.1 Pro

Google · $4.11/M

2M token context window.

What evidence supports the Image Understanding recommendation?

We have not run a controlled test for image understanding. This ranking is a requirements match on price, measured throughput, and context window — not a quality comparison. Models that fit this task's requirements are ranked; which one performs best on your prompt is a question you should answer by running it. Run all three side by side →

Reproducible Image Understanding evidence and decision rubric

Test / run	Prompt and verification	Hard rule
Code Snippet	exact prompt + 20 recorded runs	Correct iterative algorithm and code-only output
Hard Algorithm	exact prompt + 4 recorded runs	5,000-case harness; O(log n) partition and correct edge cases

Run dates: budget suite 2026-06-16T20:31:30.728Z; premium suite 2026-06-21T00:00:00.000Z. Results are not a claim about every repository or prompt.

Two-test rubric, failure analysis, and task-shaped ranking

Model	Accuracy	Latency	Output tokens	Run cost	Failure / qualification note
GPT-5.4 Pro	100/100	206116 ms	548	$0.103	Passes all 5,000+ randomized cases and every edge case; genuine O(log(min(m,n))) partition search, returns a float, code-only, and explicitly raises on two empty lists. Correct — but the slowest run by far (over three minutes), and now that real usage is reported, comfortably the most expensive.
Claude Opus 4.8	100/100	3790 ms	382	$0.011	Passes all 5,000+ randomized cases and every edge case; genuine O(log(min(m,n))) partition, float return, code-only. The fastest correct solution in this task.
GLM 5.2 (Max)	100/100	47713 ms	2148	$0.010	Passes all 5,000+ randomized cases and every edge case with a genuine O(log) partition and float return. The <think> block ahead of the code is GLM's reasoning channel surfaced by our gateway, not reasoning dumped into the answer — GLM's actual content is the clean code block — so it scores level with the other correct solutions, as the cheapest of them.
Gemini 3.1 Pro	99/100	17413 ms	320	$0.004	Passes all tests with a clean, minimal O(log) partition and float return, code-only. Docked one point only because two empty lists yield NaN rather than an explicit guard (not required by the prompt).

Availability caveat: short code tests do not establish repository-scale debugging, multi-file tool use, or agent reliability. The fastest acceptable verdict must therefore clear the correctness rule before speed is considered.

Task-shaped cost ranking (20,000 tasks/month)

Rank	Model	Effective monthly	Measured verbosity
1	Amazon Nova Micro	$18.26	0.76×
2	Amazon Nova Lite	$32.74	0.91×
3	GPT-5 Nano	$36.00	Unavailable; neutral fallback
4	Gemini 2.5 Flash Lite	$56.00	Unavailable; neutral fallback
5	GPT-6 Luna	$60.00	Unavailable; neutral fallback
6	GPT-6 Luna Pro	$60.00	Unavailable; neutral fallback

Verified 2026-08-08. full prompt/run evidence →

Try these models for Image Understanding →

Volume, requirement-gate, and pricing cross-check for Image Understanding

Monthly spend ladder at Image analysis call shape

Calls / month	Overall pick monthly	Budget pick monthly	Overall − budget delta
10,000	$3.50	$3.50	$0.0000
20,000	$7.00	$7.00	$0.0000
40,000	$14.00	$14.00	$0.0000
100,000	$35.00	$35.00	$0.0000

Monthly cost = task price/M × (1,500 input + 400 output tokens) × calls ÷ 1,000,000, at 0.5×, 1×, 2×, 5× the published 20,000-call/month baseline.

Requirement-gate margin for the picked models

Model	Requirement	Measured value	Margin / result
GPT-6 Luna	vision required	text, vision	Gate passed
GPT-6 Luna	vision required	text, vision	Gate passed
Gemini 3.1 Pro	vision required	text, vision, audio	Gate passed

Requirement gates are hard filters, not down-ranking: a model failing any row here is excluded from Image Understanding candidates entirely, regardless of price or speed.

Pricing-page cross-link for each pick

Model	Task-weighted $/M	Monthly at published volume	Measured throughput
GPT-6 Luna	$0.18	$7.00	Unavailable
Qwen 3.8 30B	$1.11	$42.00	690 tok/s
Gemini 3.1 Pro	$4.11	$156.00	55 tok/s

Evidence coverage: 0 of 44 candidates have a graded run. No graded accuracy evidence exists for this task; the ranking above is a requirements-and-price match, not a quality claim.

Test the Image Understanding picks side by side →

Verified 2026-08-08. "Unavailable" means no compatible dated evidence was found for that field; it is never treated as zero. Dated source · Full ranking and rubric

Image-understanding evidence boundary

1. Vision billing and gateway matrix

Candidate	Modality gate	Gateway/provider	Text-token task price	Image billing
GPT-6 Luna	Vision eligible	OpenAI	$0.18	Image units / resolution tier: Unavailable
GPT-6 Luna Pro	Vision eligible	OpenAI	$0.18	Image units / resolution tier: Unavailable
Gemini 2.5 Flash Lite	Vision eligible	Google	$0.16	Image units / resolution tier: Unavailable
Gemini 2.5 Flash	Vision eligible	Google	$0.76	Image units / resolution tier: Unavailable
Gemini 3.7 Flash	Vision eligible	Google	$1.38	Image units / resolution tier: Unavailable
Gemini 3.5 Flash Lite	Vision eligible	Google	$0.76	Image units / resolution tier: Unavailable

Image units, resolution tiers, and per-image prices are Unavailable unless sourced; missing image prices never become zero.

2. Multi-image context planner

Images	GPT-6 Luna	GPT-6 Luna Pro	Gemini 2.5 Flash Lite
1 images	GPT-6 Luna: 1.0M	GPT-6 Luna Pro: 1.0M	Gemini 2.5 Flash Lite: 997K
5 images	GPT-6 Luna: 1.0M	GPT-6 Luna Pro: 1.0M	Gemini 2.5 Flash Lite: 993K
10 images	GPT-6 Luna: 1.0M	GPT-6 Luna Pro: 1.0M	Gemini 2.5 Flash Lite: 988K
25 images	GPT-6 Luna: 1.0M	GPT-6 Luna Pro: 1.0M	Gemini 2.5 Flash Lite: 973K

Remaining context = sourced window − (images × declared 1,000 tokens/image) − 1,500 text tokens − 400 output tokens. Per-image tokens are an estimate, not a provider fact.

3. Task-transfer test matrix

Task	Coverage	Required run	Verdict
OCR	Unavailable	Need a dated task run with exact images and prompt	No quality verdict
Charts	Unavailable	Need a dated task run with exact images and prompt	No quality verdict
Screenshots	Unavailable	Need a dated task run with exact images and prompt	No quality verdict
Photographs	Unavailable	Need a dated task run with exact images and prompt	No quality verdict
Multi-image comparison	Unavailable	Need a dated task run with exact images and prompt	No quality verdict

A single unrelated test cannot establish OCR, chart, screenshot, photograph, or multi-image accuracy.

Verified 2026-08-08. “Unavailable” means no compatible dated evidence was found; it is never zero, an estimate, or a guessed policy. Stale-data behavior: dated registry values are snapshots. If a source is older than the page verification date, or a provider changes its policy/pricing/model, re-verify before production use; unknown values remain Unavailable. Dated registry source · Run this scenario yourself →

Which models rank highest for Image Understanding?

"Fit" is a requirements match, not a quality benchmark — it combines price, measured speed, context window, and (where we have run it) graded accuracy on this task. Formula below.

#	Model	Provider	Fit	Evidence	Task price/M	Tokens/sec	Context	Scored on
1	GPT-6 Luna	OpenAI	82	—	$0.18	—	1.1M	price, context
2	GPT-6 Luna Pro	OpenAI	82	—	$0.18	—	1.1M	price, context
3	Gemini 2.5 Flash Lite Legacy	Google	82	—	$0.16	—	1M	price, context
4	Gemini 2.5 Flash Legacy	Google	70	—	$0.76	—	1M	price, context
5	Gemini 3.7 Flash	Google	66	—	$1.38	—	1.0M	price, context
6	Gemini 3.5 Flash Lite	Google	65	—	$0.76	162	1M	price, context, speed
7	Gemini 3.1 Pro	Google	64	—	$4.11	55	2M	price, context, speed
8	Grok-3 Legacy	xAI	61	—	$2.42	—	1M	price, context

What will Image Understanding cost?

At 20,000 image analysis call calls/month:

Model	Task price/M	Est. monthly cost
GPT-6 Luna	$0.18	$7.00
GPT-6 Luna Pro	$0.18	$7.00
Gemini 2.5 Flash Lite	$0.16	$6.20

How is the best LLM for Image Understanding ranked?

Weights: evidence 0%, price 40%, speed 10%, context 50%.

Requirements: vision input. 44 models eligible.

Price and context sub-scores are min-max normalised (log-scaled) within this task's eligible set only. Speed uses measured tokens/sec only — estimated rows are excluded. A model missing a measurement is never scored as zero: its weight is redistributed across the components we do have, and "Scored on" in the table above shows exactly which ones.

Prices verified 2026-08-08.

What related resources help with Image Understanding?

OpenAI provider hub GPT-6 Luna pricing Best LLM for Coding Best LLM for Math & Reasoning Best LLM for Chatbots & Support

What are common questions about the best LLM for Image Understanding?

Does context window matter for a single image?

Mostly for multi-image or image-plus-long-text prompts — a single image and short prompt fits comfortably in any vision-capable model's window here.

What about audio or video understanding?

A few models on this page also accept audio input (noted on their pricing page) — this ranking filters on vision support specifically, since that is the more universal requirement.

Is a bigger model always more accurate on images?

Not reliably — vision accuracy depends on training, not just parameter count. We have not graded this directly; verify against your own images before committing at volume.