Part of Best LLM for Vision & Documents

Best LLM for Image Understanding

GPT-6.1 Sol ranks first for Image Understanding as of October 20, 2026.[1][2] It scores 92.63% on LiveBench Reasoning (#2 of 59), an objective, ground-truth-scored evaluation refreshed with new questions. Claude Opus 5.5 scores within two points, so read the top two as close rather than as a measured gap. Claude Opus 5.5 is the next-ranked alternative. It scores 92.15% on LiveBench Reasoning (#3 of 59), an objective, ground-truth-scored evaluation refreshed with new questions.

About this recommendation

Updated
Evidence through
Sources
9
Revision
v106

Decision audit

Why this result

Inspect the inputs and the computed order behind the recommendation.

Models screened

24

live candidates

Evaluation feeds

3

task-weighted

Winner coverage

61%

intended feed weight

Largest provider share

2 of 8

Anthropic

Established source breadth. 5 citation families and 0 practitioner families provide attributed capability reports; 0 cautionary threads retained. The largest citation family contributes 42%.

OpenAI: GPT-6.1 Sol leads the measured task comparison with 2 independent evaluation families. Community volume, release age and usage popularity add no ranking points. The leading scores are close under this policy.

Real-world track record is still developing. The recommendation can lead on independent measurements while practitioner reports accumulate.

Sources evaluated

These are the weights used in scoring. Missing measurements are omitted from each model’s average. A feed with no result for any model released in the last 60 days is stale and does not score until it catches up.

winner: GPT-6.1 Sol
Evaluation feedWeightWinner resultField measured
LMArena VisionObserved 2026-10-19 · evaluation date not supplied · max effort
82%
#524/24
VLMEvalKit tasksdisabled; last successful check unknownunavailable
0%
feed unavailable0/24
LiveBench Reasoning (2026-06-25)Observed 2026-10-18 · evaluation date not supplied · gpt-6.1-sol-maxfailing; last successful check 2026-10-18
18%
#224/24

Provider concentration

Each exact model is scored separately; provider identity is not a ranking input.

Anthropic25%
  • Anthropic2 models
  • Google2 models
  • Meta2 models
  • OpenAI2 models

Decision table

Every published model is shown in computed order. Practitioner sources are distinct community threads, not the citations repeated in the prose below.

  1. 01GPT-6.1 Sol100

    OpenAI · coverage 61%

    #2 LiveBench Reasoning · #5 LMArena Vision

  2. 02Claude Opus 5.599

    Anthropic · coverage 61% · close score

    #3 LiveBench Reasoning · #6 LMArena Vision

  3. 03Muse Spark 1.298

    Meta · coverage 61% · close score

    #6 LMArena Vision · #11 LiveBench Reasoning

  4. 04Gemini 3.7 Flash97

    Google · coverage 61%

    #4 LMArena Vision · #21 LiveBench Reasoning

  5. 05Claude Fable 5.197

    Anthropic · coverage 61%

    #4 LiveBench Reasoning · #10 LMArena Vision

  6. 06Gemini 3.8 Flash97

    Google · coverage 61%

    #8 LMArena Vision · #15 LiveBench Reasoning

  7. 07Muse Spark 1.396

    Meta · coverage 61%

    #9 LMArena Vision · #12 LiveBench Reasoning

  8. 08GPT-6 Astra96

    OpenAI · coverage 61%

    #1 LiveBench Reasoning · #14 LMArena Vision

Task score averages the available, task-weighted measurements. Benchmark positions compare models matched to our catalog. Models measured by at least two independent evaluator families are considered first. Missing tests, practitioner report counts, popularity and release age add no points. Coverage describes evidence breadth, not a probability of correctness. Scores within two points are shown as close under this policy; this is not a statistical confidence interval. When the leading scores are that close, the model measured by more evaluator families ranks first.

  1. 01

    GPT-6.1 SolOpenAI · $2 in / $10 out per M tokens · 1.1M context

    GPT-6.1 Sol scores 92.63% on LiveBench Reasoning (#2 of 59), an objective, ground-truth-scored evaluation refreshed with new questions.

    Best when: Its strongest result for this task is on LiveBench Reasoning, which measures multi-step reasoning.
    Source 1
    “Within the 59 catalog models matched to this evaluation: Scores 92.63% on LiveBench Reasoning (#2 of 59), an objective, ground-truth-scored evaluation refreshed with new questions.”
    LiveBench ReasoningOpen original ↗

    No practitioner reports are linked yet, so no cautions are listed. Absence of reports is not evidence of reliability.

  2. 02

    Claude Opus 5.5Anthropic · $4 in / $20 out per M tokens · 1M context

    Claude Opus 5.5 scores 92.15% on LiveBench Reasoning (#3 of 59), an objective, ground-truth-scored evaluation refreshed with new questions.

    Best when: Its strongest result for this task is on LiveBench Reasoning, which measures multi-step reasoning.
    Source 2
    “Within the 59 catalog models matched to this evaluation: Scores 92.15% on LiveBench Reasoning (#3 of 59), an objective, ground-truth-scored evaluation refreshed with new questions.”
    LiveBench ReasoningOpen original ↗

    No practitioner reports are linked yet, so no cautions are listed. Absence of reports is not evidence of reliability.

  3. 03

    Muse Spark 1.2Meta · $1.25 in / $4.25 out per M tokens · 1.0M context

    Muse Spark 1.2 ties for #6 of 81 on LMArena's vision arena (Elo 1293) but drops to #11 of 59 on LiveBench Reasoning at 90%, showing strong human preference with moderate structured reasoning.

    Best when: Use for image description tasks where human preference rankings guide selection, tying at Elo 1293 (#6 of 81) on LMArena's vision arena.
    Source 3
    “Within the 81 catalog models matched to this evaluation: Ranks #6 of 81 on LMArena's vision arena (Elo 1293), based on human preference on image-understanding tasks.”
    LMArena vision arenaOpen original ↗

    Watch out for

    • Expect weaker performance on structured visual reasoning benchmarks, scoring only 90% on LiveBench Reasoning (#11 of 59).
      Source 4
      “Within the 59 catalog models matched to this evaluation: Scores 90% on LiveBench Reasoning (#11 of 59), an objective, ground-truth-scored evaluation refreshed with new questions.”
      LiveBench ReasoningOpen original ↗
  4. 04

    Gemini 3.7 FlashGoogle · $0.75 in / $3.75 out per M tokens · 1.0M context

    Gemini 3.7 Flash ranks #4 of 81 on LMArena's vision arena (Elo 1295), based on human preference on image-understanding tasks.

    Best when: Its strongest result for this task is on LMArena Vision, which measures reading images, screenshots, and documents.
    Source 5
    “Within the 81 catalog models matched to this evaluation: Ranks #4 of 81 on LMArena's vision arena (Elo 1295), based on human preference on image-understanding tasks.”
    LMArena vision arenaOpen original ↗

    No practitioner reports are linked yet, so no cautions are listed. Absence of reports is not evidence of reliability.

  5. 05

    Claude Fable 5.1Anthropic · $10 in / $50 out per M tokens · 1M context

    Claude Fable 5.1 ranks #10 of 81 on LMArena's vision arena (Elo 1288) and demonstrates strong 3D spatial reasoning with precise brush tool control, generating complex visual outputs at $0.261 per task.

    Best when: Use for 3D spatial visualization tasks requiring precise coordinate control, as it successfully drew an Airbus A320 in 3D space with one brush tool controlling x,y,z coordinates, color, size, and hardness.
    Source 6
    “how about trying to draw an airbus a320 in 3d space using only one brush tool that can be moved to specific x,y,z coordinates (and its color, size & hardness can be changed). i think fable 5.1 did quite a good job (reasoning high, cost $0,261): https: files.catbox.moe umx102.png for comparision, this is fable 5: https: files.catbox.moe ihl4m1.png”
    leumonOpen original ↗

    More tips

    • Consider for cost-sensitive visual generation, with reported task cost of $0.261 for complex 3D drawing.
      Source 6
      “how about trying to draw an airbus a320 in 3d space using only one brush tool that can be moved to specific x,y,z coordinates (and its color, size & hardness can be changed). i think fable 5.1 did quite a good job (reasoning high, cost $0,261): https: files.catbox.moe umx102.png for comparision, this is fable 5: https: files.catbox.moe ihl4m1.png”
      leumonOpen original ↗
  6. 06

    Gemini 3.8 FlashGoogle · $0.83 in / $4.13 out per M tokens · 1.0M context

    Gemini 3.8 Flash ranks #8 of 81 on LMArena's vision arena (Elo 1291) and demonstrates production capability processing handwritten field revisions in shipyard PDFs, though integration issues exist with certain attachment formats.

    Best when: Deploy for document analysis with handwritten content, as it processes handwritten field revisions in unstructured shipyard PDFs.
    Source 7
    “Overview Upgrade scripts/prototype_experiment.py from local regex parsing to a live multimodal pipeline utilizing the Gemini 3.8 Flash API, automating the extraction of unstructured shipyard PDFs (including handwritten notes and non-standard tables) and drafting Lloyd's-standard Preliminary Survey Reports from deterministic appraisal outputs. Context & Motivation - Shipyard repair specifications vary widely in layout, trade nomenclature, and often include handwritten field revisions…”
    yohei1126Open original ↗
  7. 07

    Muse Spark 1.3Meta · $1.25 in / $4.25 out per M tokens · 1.0M context

    Muse Spark 1.3 ranks #9 of 81 on LMArena's vision arena (Elo 1290), based on human preference on image-understanding tasks.

    Best when: Its strongest result for this task is on LMArena Vision, which measures reading images, screenshots, and documents.
    Source 8
    “Within the 81 catalog models matched to this evaluation: Ranks #9 of 81 on LMArena's vision arena (Elo 1290), based on human preference on image-understanding tasks.”
    LMArena vision arenaOpen original ↗
  8. 08

    GPT-6 AstraOpenAI · $10 in / $50 out per M tokens · 1.1M context

    GPT-6 Astra scores 92.65% on LiveBench Reasoning (#1 of 59), an objective, ground-truth-scored evaluation refreshed with new questions.

    Best when: Its strongest result for this task is on LiveBench Reasoning, which measures multi-step reasoning.
    Source 9
    “Within the 59 catalog models matched to this evaluation: Scores 92.65% on LiveBench Reasoning (#1 of 59), an objective, ground-truth-scored evaluation refreshed with new questions.”
    LiveBench ReasoningOpen original ↗

    No practitioner reports are linked yet, so no cautions are listed. Absence of reports is not evidence of reliability.

Frequently asked

What is the best model for Image Understanding?
GPT-6.1 Sol ranks first as of October 20, 2026. GPT-6.1 Sol scores 92.63% on LiveBench Reasoning (#2 of 59), an objective, ground-truth-scored evaluation refreshed with new questions.[1]
What is the best alternative to GPT-6.1 Sol?
Claude Opus 5.5 ranks second. Claude Opus 5.5 scores 92.15% on LiveBench Reasoning (#3 of 59), an objective, ground-truth-scored evaluation refreshed with new questions.[2]

Sources

  1. 1

    “Within the 59 catalog models matched to this evaluation: Scores 92.63% on LiveBench Reasoning (#2 of 59), an objective, ground-truth-scored evaluation refreshed with new questions.”

    LiveBench Reasoning · Benchmark · Oct 18, 2026
  2. 2

    “Within the 59 catalog models matched to this evaluation: Scores 92.15% on LiveBench Reasoning (#3 of 59), an objective, ground-truth-scored evaluation refreshed with new questions.”

    LiveBench Reasoning · Benchmark · Oct 18, 2026
  3. 3

    “Within the 81 catalog models matched to this evaluation: Ranks #6 of 81 on LMArena's vision arena (Elo 1293), based on human preference on image-understanding tasks.”

    LMArena vision arena · Benchmark · Oct 19, 2026
  4. 4

    “Within the 59 catalog models matched to this evaluation: Scores 90% on LiveBench Reasoning (#11 of 59), an objective, ground-truth-scored evaluation refreshed with new questions.”

    LiveBench Reasoning · Benchmark · Oct 18, 2026
  5. 5

    “Within the 81 catalog models matched to this evaluation: Ranks #4 of 81 on LMArena's vision arena (Elo 1295), based on human preference on image-understanding tasks.”

    LMArena vision arena · Benchmark · Oct 19, 2026
  6. 6

    “how about trying to draw an airbus a320 in 3d space using only one brush tool that can be moved to specific x,y,z coordinates (and its color, size & hardness can be changed). i think fable 5.1 did quite a good job (reasoning high, cost $0,261): https: files.catbox.moe umx102.png for comparision, this is fable 5: https: files.catbox.moe ihl4m1.png”

    leumon · Hacker News · Sep 1, 2026
  7. 7

    “Overview Upgrade scripts/prototype_experiment.py from local regex parsing to a live multimodal pipeline utilizing the Gemini 3.8 Flash API, automating the extraction of unstructured shipyard PDFs (including handwritten notes and non-standard tables) and drafting Lloyd's-standard Preliminary Survey Reports from deterministic appraisal outputs. Context & Motivation - Shipyard repair specifications vary widely in layout, trade nomenclature, and often include handwritten field revisions…”

    yohei1126 · GitHub · Sep 26, 2026
  8. 8

    “Within the 81 catalog models matched to this evaluation: Ranks #9 of 81 on LMArena's vision arena (Elo 1290), based on human preference on image-understanding tasks.”

    LMArena vision arena · Benchmark · Oct 19, 2026
  9. 9

    “Within the 59 catalog models matched to this evaluation: Scores 92.65% on LiveBench Reasoning (#1 of 59), an objective, ground-truth-scored evaluation refreshed with new questions.”

    LiveBench Reasoning · Benchmark · Oct 18, 2026

Rankings synthesized from community evidence and open benchmarks. See methodology. Not driven by vendor marketing.