Noometry

Model comparison

Claude Opus 4.7 vs Llama-3.3-70B-Instruct

Claude Opus 4.7 is the stronger model overall, scoring 58.3 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 65× less per token, which makes it the better buy when Claude Opus 4.7's lead doesn't matter for your workload.

Last verified . 28 shared benchmarks.

Claude Opus 4.7 Anthropic

58.3

Rank #19 Confirmed

Llama-3.3-70B-Instruct Meta

30.6

Rank #291 Confirmed

Summary

  • They share 28 benchmarks with published results for both. Claude Opus 4.7 scores higher in 9 categories and Llama-3.3-70B-Instruct in 0 categories; 9 gaps are clear of the uncertainty.
  • The widest gap is in math, where Claude Opus 4.7 leads 66.7 to 15.3.
  • The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 97.8% for Claude Opus 4.7 and 5.1% for Llama-3.3-70B-Instruct.
  • Llama-3.3-70B-Instruct is cheaper at $0.10 / $0.32 per million input/output tokens, against $5 / $25 for Claude Opus 4.7.
  • Claude Opus 4.7 accepts more context: 1M tokens versus 128K.
  • Llama-3.3-70B-Instruct has downloadable open weights; the other is API-only.

Side by side

Claude Opus 4.7Llama-3.3-70B-Instruct
ProviderAnthropicMeta
Noometry Index58.330.6
Released2026-04-142024-12-06
WeightsProprietaryOpen
Context window1M128K
Max output128K4K
Input $ / M tokens$5$0.10
Output $ / M tokens$25$0.32
Results tracked6643

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Category by category

Coding Claude Opus 4.7 leads

Claude Opus 4.7: 59.6 (#13), Llama-3.3-70B-Instruct: 31.0 (#290)

BenchmarkClaude Opus 4.7Llama-3.3-70B-Instruct
SciCode54.5%26%
WeirdML76.4%14.4%
LMArena Coding15181268
SWE-bench Verified83.5%—
FrontierCode38.5%—
LMArena WebDev1558—
GSO44.1%—
BigCodeBench Instruct—46.9%
LiveBench Coding—36.6%
MirrorCode31.1%—
BigCodeBench Complete—57.5%
ALE-Bench1,323—

Agentic & Tool Use Claude Opus 4.7 leads

Claude Opus 4.7: 47.9 (#10), Llama-3.3-70B-Instruct: 25.8 (#105)

BenchmarkClaude Opus 4.7Llama-3.3-70B-Instruct
Terminal-Bench80.2%—
APEX-Agents49.2%—
Berkeley Function Calling Leaderboard—31.9%
OSWorld 2.018.2%—
τ²-bench Banking40.2%—
PostTrainBench28.6%—
BALROG—23%
ExploitBench26.5%—
GBAEval43.8%—
GDP.pdf21%—
LMArena Search1233—
Vending-Bench 210,937—

Reasoning Claude Opus 4.7 leads

Claude Opus 4.7: 53.8 (#29), Llama-3.3-70B-Instruct: 14.1 (#327)

BenchmarkClaude Opus 4.7Llama-3.3-70B-Instruct
SimpleBench61.7%19.9%
CritPt12%0%
LMArena Hard Prompts15061257
DTBench94.7%59.5%
LMCA52.2%17.5%
Epoch Capabilities Index156.25127.33
ForecastBench60.358.6
ARC-AGI-275.8%—
Kagi LLM Benchmark80.7%—
NYT Connections (extended)39%—
ARC-AGI-193.5%—
Chess Puzzles30%—
Thematic Generalization72.8%—
EBR-Bench19%—
LiveBench Reasoning—50.8%
Mystery Game Puzzles28%—
LiveBench Data Analysis—49.5%
LiveBench—50.2%

Math Claude Opus 4.7 leads

Claude Opus 4.7: 66.7 (#26), Llama-3.3-70B-Instruct: 15.3 (#298)

BenchmarkClaude Opus 4.7Llama-3.3-70B-Instruct
OTIS Mock AIME 2024-202597.8%5.1%
LMArena Math14991267
FrontierMath (Tiers 1-3)70.2%—
FrontierMath Tier 431.7%—
MathArena Final-Answer Competitions73.6%—
ProofBench54%—
LiveBench Math—42.2%
MATH Level 5—41.6%
FrontierMath (Feb 2025 set)43.8%—
FrontierMath Tier 4 (v1)22.9%—

Knowledge Claude Opus 4.7 leads

Claude Opus 4.7: 62.6 (#23), Llama-3.3-70B-Instruct: 30.6 (#226)

BenchmarkClaude Opus 4.7Llama-3.3-70B-Instruct
GPQA Diamond90.2%47.4%
Vectara Hallucination Rate12%4.1%
LMArena Expert15211225
Humanity's Last Exam36.2%—
SimpleQA Verified51.7%—
Confabulations—22.8%
MMLU—86.3%

Multimodal Not comparable

Claude Opus 4.7: 41.2 (#38), Llama-3.3-70B-Instruct: —

BenchmarkClaude Opus 4.7Llama-3.3-70B-Instruct
LMArena Vision1316—
Blueprint-Bench 224.5%—
Furniture Assembly33.3%—
LMArena Document1495—

Multilingual Claude Opus 4.7 leads

Claude Opus 4.7: 57.3 (#10), Llama-3.3-70B-Instruct: 39.9 (#220)

BenchmarkClaude Opus 4.7Llama-3.3-70B-Instruct
LMArena Non-English14801236
LMArena Chinese15311217
LMArena French15031281
LMArena German14951251
LMArena Japanese14721150
LMArena Korean14641143
LMArena Russian14941252
LMArena Spanish14951270

Instruction Following Claude Opus 4.7 leads

Claude Opus 4.7: 78.4 (#10), Llama-3.3-70B-Instruct: 71.1 (#157)

BenchmarkClaude Opus 4.7Llama-3.3-70B-Instruct
LMArena Instruction Following14981242
LiveBench Instruction Following—82.7%

Long Context Claude Opus 4.7 leads

Claude Opus 4.7: 46.2 (#25), Llama-3.3-70B-Instruct: 26.4 (#295)

BenchmarkClaude Opus 4.7Llama-3.3-70B-Instruct
LMArena Longer Query15051256
Fiction.LiveBench—33.3%

Writing & Preference Claude Opus 4.7 leads

Claude Opus 4.7: 75.1 (#8), Llama-3.3-70B-Instruct: 47.6 (#207)

BenchmarkClaude Opus 4.7Llama-3.3-70B-Instruct
LMArena Text14901274
LMArena Creative Writing14861250
LMArena Multi-Turn15051280
EQ-Bench Creative Writing1914—
EQ-Bench 41311—
LiveBench Language—39.2%

Frequently asked questions

Is Claude Opus 4.7 better than Llama-3.3-70B-Instruct?

Claude Opus 4.7 is the stronger model overall, scoring 58.3 to 30.6 on the Noometry Index. Llama-3.3-70B-Instruct costs 65× less per token, which makes it the better buy when Claude Opus 4.7's lead doesn't matter for your workload.

Which is cheaper, Claude Opus 4.7 or Llama-3.3-70B-Instruct?

Llama-3.3-70B-Instruct is cheaper. It lists at $0.10 per million input tokens and $0.32 per million output tokens; Claude Opus 4.7 lists at $5 and $25.

Is Claude Opus 4.7 or Llama-3.3-70B-Instruct better for coding?

Claude Opus 4.7 scores higher on coding benchmarks: 59.6 versus 31.0 in the Noometry coding category.

Which has the bigger context window?

Claude Opus 4.7 does, with 1M tokens against 128K.

How many benchmarks do Claude Opus 4.7 and Llama-3.3-70B-Instruct share?

28 benchmarks have published results for both models. Claude Opus 4.7 has 66 scored results on Noometry and Llama-3.3-70B-Instruct has 43.

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