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Claude Opus 5.5 vs Llama 3.2 1B Instruct

Claude Opus 5.5 leads Llama 3.2 1B Instruct on the Artificial Analysis Intelligence Index, 57.6 to 4.8, and Llama 3.2 1B Instruct costs 99.5x less per output token ($0.201 vs $20.00 per million). Updated October 21, 2026; 27 benchmarks have a result for both.

Claude Opus 5.5Anthropic
Coding
88.8
Overall
87.1
Output $/M
$20.00
27
Llama 3.2 1B InstructMeta Llama
Coding
No data
Overall
9.6
Output $/M
$0.201
0
vs boards won of 27 shared

Claude Opus 5.5 or Llama 3.2 1B Instruct: which to pick

Only Claude Opus 5.5 has a coding score, 88.8 on the Vibe Coding Index; Llama 3.2 1B Instruct has none, so the two cannot be ranked on coding.

Pick Llama 3.2 1B Instruct for output tokens 99.5x cheaper ($0.201 vs $20.00 per million).

Shared results
27
Claude Opus 5.5 only
82
Llama 3.2 1B Instruct only
11
Like-for-like categories
6 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
Claude Opus 5.5 88.8 from 5 benchmarks, Llama 3.2 1B Instruct not benchmarked.
Widest category
General, 81.8 points apart on average over 12 shared benchmarks.
Widest single gap
OTIS Mock AIME: Claude Opus 5.5 100.0% against Llama 3.2 1B Instruct 0.6%, 99.4 points apart on a 0 to 100 scale.
Output price
Claude Opus 5.5 $20.00 against Llama 3.2 1B Instruct $0.201 per million output tokens, 99.5x apart.
Context window
Claude Opus 5.5 takes 1M tokens, Llama 3.2 1B Instruct 60K.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryClaude Opus 5.5BarsLlama 3.2 1B InstructTies
Coding20
Intelligence10
Reasoning50
Math30
Knowledge40
General120
All shared boards270
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
Coding benchmarks for Claude Opus 5.5 and Llama 3.2 1B Instruct
BenchmarkClaude Opus 5.5Llama 3.2 1B InstructLeader
LiveBench coding96.3%Not measuredOnly Claude Opus 5.5 measured
LMArena WebDev1807Not measuredOnly Claude Opus 5.5 measured
Terminal-Bench 4 (Vals AI)65.2%Not measuredOnly Claude Opus 5.5 measured
Vibe Code Bench (Vals AI)90.3%Not measuredOnly Claude Opus 5.5 measured
Code migration (Vals AI)66.7%Not measuredOnly Claude Opus 5.5 measured
ProgramBench (Vals AI)18.5%Not measuredOnly Claude Opus 5.5 measured
IOI (Vals AI)95.1%Not measuredOnly Claude Opus 5.5 measured
Terminal-Bench 2.1 (Vals AI)87.6%Not measuredOnly Claude Opus 5.5 measured
SWE-bench Multimodal (Vals AI)70.2%Not measuredOnly Claude Opus 5.5 measured
Vibe Code Bench 1-100 (Vals AI)30.4%Not measuredOnly Claude Opus 5.5 measured
CyberBench (Vals AI)74.6%Not measuredOnly Claude Opus 5.5 measured
LiveCodeBench (Artificial Analysis run)Not measured1.9%Only Llama 3.2 1B Instruct measured
SciCode66.9%Not measuredOnly Claude Opus 5.5 measured
Terminal-Bench Hard (Artificial Analysis run)Not measured0.0%Only Llama 3.2 1B Instruct measured
FrontierCode54.6%Not measuredOnly Claude Opus 5.5 measured
FrontierSWE62.3%Not measuredOnly Claude Opus 5.5 measured
MirrorCode77.4%Not measuredOnly Claude Opus 5.5 measured
ALE-Bench2147.4Not measuredOnly Claude Opus 5.5 measured
LMArena Text: coding15451070Claude Opus 5.5
LMArena Text: software and IT15381056Claude Opus 5.5
LMArena WebDev: React1855Not measuredOnly Claude Opus 5.5 measured
LMArena WebDev: HTML1753Not measuredOnly Claude Opus 5.5 measured
LMArena WebDev: from an image1753Not measuredOnly Claude Opus 5.5 measured
SciCode18.5%Not measuredOnly Claude Opus 5.5 measured
Neither model has a result on the other 15 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for Claude Opus 5.5 and Llama 3.2 1B Instruct
BenchmarkClaude Opus 5.5Llama 3.2 1B InstructLeader
AA Intelligence Index57.64.8Claude Opus 5.5
Epoch Capabilities Index168.8Not measuredOnly Claude Opus 5.5 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index26 boards, none shared
Agentic work benchmarks for Claude Opus 5.5 and Llama 3.2 1B Instruct
BenchmarkClaude Opus 5.5Llama 3.2 1B InstructLeader
Design Arena: agents building web apps1317Not measuredOnly Claude Opus 5.5 measured
Design Arena: agents building mobile apps1310Not measuredOnly Claude Opus 5.5 measured
Design Arena: spreadsheets1352Not measuredOnly Claude Opus 5.5 measured
LiveBench research85.0%Not measuredOnly Claude Opus 5.5 measured
LiveBench browser80.0%Not measuredOnly Claude Opus 5.5 measured
LiveBench long agentic85.0%Not measuredOnly Claude Opus 5.5 measured
SREBench (Vals AI)33.6%Not measuredOnly Claude Opus 5.5 measured
Finance Agent (Vals AI)58.6%Not measuredOnly Claude Opus 5.5 measured
Tax Agent Bench (Vals AI)45.6%Not measuredOnly Claude Opus 5.5 measured
Legal Research Bench (Vals AI)50.5%Not measuredOnly Claude Opus 5.5 measured
Harvey's Legal Agent Benchmark (Vals AI)9.2%Not measuredOnly Claude Opus 5.5 measured
Excel Modeling Benchmark (Vals AI)75.9%Not measuredOnly Claude Opus 5.5 measured
BioMysteryBench (Vals AI)79.3%Not measuredOnly Claude Opus 5.5 measured
Terminal-Bench-Science (Vals AI)47.1%Not measuredOnly Claude Opus 5.5 measured
Vals Science Index58.7%Not measuredOnly Claude Opus 5.5 measured
Time Horizon Index (Vals AI)91.3%Not measuredOnly Claude Opus 5.5 measured
CUA-bench (Vals AI)14.0%Not measuredOnly Claude Opus 5.5 measured
Robo-bench (Vals AI)14.6%Not measuredOnly Claude Opus 5.5 measured
τ²-Bench TelecomNot measured0.0%Only Llama 3.2 1B Instruct measured
APEX-Agents73.5%Not measuredOnly Claude Opus 5.5 measured
Remote Labor Index21.3%Not measuredOnly Claude Opus 5.5 measured
BALROGNot measured6.6%Only Llama 3.2 1B Instruct measured
SpireClimb3.3%Not measuredOnly Claude Opus 5.5 measured
Vending-Bench 2$9,235Not measuredOnly Claude Opus 5.5 measured
LMArena Search1213Not measuredOnly Claude Opus 5.5 measured
Berkeley Function Calling Leaderboard (BFCL)Not measured10.8%Only Llama 3.2 1B Instruct measured
Neither model has a result on the other 15 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
Reasoning benchmarks for Claude Opus 5.5 and Llama 3.2 1B Instruct
BenchmarkClaude Opus 5.5Llama 3.2 1B InstructLeader
LiveBench reasoning92.2%Not measuredOnly Claude Opus 5.5 measured
MysteryMechanism (Vals AI)49.5%Not measuredOnly Claude Opus 5.5 measured
GPQA Diamond (Artificial Analysis run)Not measured19.6%Only Llama 3.2 1B Instruct measured
Humanity's Last Exam (Artificial Analysis run)61.4%5.5%Claude Opus 5.5
GPQA Diamond (Epoch AI run)90.6%23.9%Claude Opus 5.5
ARC-AGI98.5%Not measuredOnly Claude Opus 5.5 measured
ARC-AGI-293.3%Not measuredOnly Claude Opus 5.5 measured
Chess puzzles67.0%Not measuredOnly Claude Opus 5.5 measured
Mystery game puzzles71.0%Not measuredOnly Claude Opus 5.5 measured
EBR-bench71.4%Not measuredOnly Claude Opus 5.5 measured
LMArena Text: hard prompts15361044Claude Opus 5.5
LMArena Text: expert15491007Claude Opus 5.5
LMArena Text: hard prompts in English15351087Claude Opus 5.5
LMArena Vision: diagrams1345Not measuredOnly Claude Opus 5.5 measured
LMArena Vision: homework1362Not measuredOnly Claude Opus 5.5 measured
Neither model has a result on the other 7 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for Claude Opus 5.5 and Llama 3.2 1B Instruct
BenchmarkClaude Opus 5.5Llama 3.2 1B InstructLeader
AA Math IndexNot measured0.0Only Llama 3.2 1B Instruct measured
AIME 2025 (Artificial Analysis run)Not measured0.0%Only Llama 3.2 1B Instruct measured
OTIS Mock AIME100.0%0.6%Claude Opus 5.5
FrontierMath, tiers 1 to 391.2%Not measuredOnly Claude Opus 5.5 measured
FrontierMath, tier 495.0%Not measuredOnly Claude Opus 5.5 measured
ProofBench100.0%Not measuredOnly Claude Opus 5.5 measured
LMArena Text: math15181086Claude Opus 5.5
LMArena Text: mathematical work15161098Claude Opus 5.5
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for Claude Opus 5.5 and Llama 3.2 1B Instruct
BenchmarkClaude Opus 5.5Llama 3.2 1B InstructLeader
MedCode (Vals AI)49.8%Not measuredOnly Claude Opus 5.5 measured
MMLU Pro (Artificial Analysis run)Not measured20.0%Only Llama 3.2 1B Instruct measured
SimpleQA Verified72.2%Not measuredOnly Claude Opus 5.5 measured
LMArena Text: science15241055Claude Opus 5.5
LMArena Text: medicine and healthcare15091009Claude Opus 5.5
LMArena Text: legal and government15181071Claude Opus 5.5
LMArena Text: business and finance14941000Claude Opus 5.5
FACTS Benchmark Suite65.3%Not measuredOnly Claude Opus 5.5 measured
Neither model has a result on the other 10 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for Claude Opus 5.5 and Llama 3.2 1B Instruct
BenchmarkClaude Opus 5.5Llama 3.2 1B InstructLeader
Design Arena: websites1336Not measuredOnly Claude Opus 5.5 measured
Design Arena: UI components1392Not measuredOnly Claude Opus 5.5 measured
Design Arena: game dev1458Not measuredOnly Claude Opus 5.5 measured
Design Arena: data viz1397Not measuredOnly Claude Opus 5.5 measured
Design Arena: 3D1477Not measuredOnly Claude Opus 5.5 measured
Design Arena: all code categories1390Not measuredOnly Claude Opus 5.5 measured
Design Arena: SVG1333Not measuredOnly Claude Opus 5.5 measured
LiveBench design86.9%Not measuredOnly Claude Opus 5.5 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index120
General benchmarks for Claude Opus 5.5 and Llama 3.2 1B Instruct
BenchmarkClaude Opus 5.5Llama 3.2 1B InstructLeader
Vals Index70.2%Not measuredOnly Claude Opus 5.5 measured
LiveBench data analysis80.3%Not measuredOnly Claude Opus 5.5 measured
Public Benefits Bench (Vals AI)70.6%Not measuredOnly Claude Opus 5.5 measured
SAGE (Vals AI)45.8%Not measuredOnly Claude Opus 5.5 measured
MedScribe (Vals AI)91.4%Not measuredOnly Claude Opus 5.5 measured
IFBenchNot measured22.8%Only Llama 3.2 1B Instruct measured
Long context reasoning (AA-LCR)84.7%6.7%Claude Opus 5.5
DTBench98.9%Not measuredOnly Claude Opus 5.5 measured
LMCA68.2%Not measuredOnly Claude Opus 5.5 measured
Furniture assembly83.3%Not measuredOnly Claude Opus 5.5 measured
LMArena Text15161055Claude Opus 5.5
LMArena Text: instruction following15371031Claude Opus 5.5
LMArena Text: creative writing15351033Claude Opus 5.5
LMArena Vision1321Not measuredOnly Claude Opus 5.5 measured
LMArena Text: English15191117Claude Opus 5.5
LMArena Text: non-English1507973Claude Opus 5.5
LMArena Text: Chinese1582959Claude Opus 5.5
LMArena Text: GermanNot measured1014Only Llama 3.2 1B Instruct measured
LMArena Text: Japanese1534Not measuredOnly Claude Opus 5.5 measured
LMArena Text: French1493Not measuredOnly Claude Opus 5.5 measured
LMArena Text: Spanish1508Not measuredOnly Claude Opus 5.5 measured
LMArena Text: Russian1517941Claude Opus 5.5
LMArena Text: multi-turn15011030Claude Opus 5.5
LMArena Text: longer queries15321050Claude Opus 5.5
LMArena Text: writing and language15321030Claude Opus 5.5
LMArena Text: entertainment and media15151025Claude Opus 5.5
LMArena Vision: English1317Not measuredOnly Claude Opus 5.5 measured
LMArena Vision: reading text1326Not measuredOnly Claude Opus 5.5 measured
LMArena Vision: creative writing1320Not measuredOnly Claude Opus 5.5 measured
Neither model has a result on the other 17 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Claude Opus 5.5 and Llama 3.2 1B Instruct
BenchmarkClaude Opus 5.5Llama 3.2 1B InstructLeader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

Claude Opus 5.5$0.014Fits in one request
Llama 3.2 1B Instruct$0.0001Fits in one request

Llama 3.2 1B Instruct costs less

Repository review

50K input + 3K output tokens

Claude Opus 5.5$0.260Fits in one request
Llama 3.2 1B Instruct$0.0020Fits in one request

Llama 3.2 1B Instruct costs less

Agent loop

200K input + 10K output tokens

Claude Opus 5.5$1.00Fits in one request
Llama 3.2 1B Instruct$0.0074Exceeds the context window

Llama 3.2 1B Instruct costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of Claude Opus 5.5 and Llama 3.2 1B Instruct
SpecificationAnthropic Claude Opus 5.5Meta Llama 3.2 1B Instruct
MakerAnthropicMeta Llama
WeightsNo public weights listedOpen weights
ReleasedSeptember 22, 2026September 25, 2024
Context window1M60K
Input $/Mtok$4.00$0.027
Output $/Mtok$20.00$0.201
Coding index88.8Not benchmarked
Independent measurements behind it50
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Claude Opus 5.5 better than Llama 3.2 1B Instruct for coding?

Only Claude Opus 5.5 has a coding score, 88.8 on the Vibe Coding Index; Llama 3.2 1B Instruct has none, so the two cannot be ranked on coding. Pick Llama 3.2 1B Instruct for output tokens 99.5x cheaper ($0.201 vs $20.00 per million).

Which is cheaper, Claude Opus 5.5 or Llama 3.2 1B Instruct?

Claude Opus 5.5 costs $4.00 per million input tokens and $20.00 per million output tokens; Llama 3.2 1B Instruct costs $0.027 and $0.201. Cost per solved task is known for neither of them.

Which has the bigger context window, Claude Opus 5.5 or Llama 3.2 1B Instruct?

Claude Opus 5.5, with 1M tokens against 60K.

How many benchmarks is the Claude Opus 5.5 vs Llama 3.2 1B Instruct comparison based on?

27 benchmarks have a result for both models as of October 21, 2026. Claude Opus 5.5's coding index rests on 5 independent benchmarks and Llama 3.2 1B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.

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