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Llama 3.1 70B Instruct vs o1

o1 leads Llama 3.1 70B Instruct on the Artificial Analysis Intelligence Index, 15.2 to 6.6, and Llama 3.1 70B Instruct costs 107.1x less per output token ($0.56 vs $60.00 per million). Updated October 21, 2026; 42 benchmarks have a result for both.

Replace Llama 3.1 70B Instruct Replace o1
Llama 3.1 70B InstructMeta Llama
Coding
No data
Overall
6.6
Output $/M
$0.56
0
o1OpenAI
Coding
No data
Overall
15.2
Output $/M
$60.00
42
vs boards won of 42 shared

Llama 3.1 70B Instruct or o1: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick Llama 3.1 70B Instruct for output tokens 107.1x cheaper ($0.56 vs $60.00 per million) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Model ALlama 3.1 70B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved taskModel Bo1OpenAINot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
42
Llama 3.1 70B Instruct only
5
o1 only
26
Like-for-like categories
7 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
Llama 3.1 70B Instruct not benchmarked, o1 not benchmarked.
Widest category
Agentic work, 47.4 points apart on average over 1 shared benchmark.
Widest single gap
OTIS Mock AIME: Llama 3.1 70B Instruct 3.6% against o1 73.3%, 69.7 points apart on a 0 to 100 scale.
Output price
Llama 3.1 70B Instruct $0.56 against o1 $60.00 per million output tokens, 107.1x apart.
Context window
Llama 3.1 70B Instruct takes 131K tokens, o1 200K.

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
CategoryLlama 3.1 70B InstructBarso1Ties
Coding05
Intelligence02
Agentic work01
Reasoning06
Math04
Knowledge05
General019
All shared boards042
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
Coding benchmarks for Llama 3.1 70B Instruct and o1
BenchmarkLlama 3.1 70B Instructo1Leader
Aider polyglotNot measured61.7%Only o1 measured
AA Coding IndexNot measured39.7Only o1 measured
LiveCodeBench (Vals AI)Not measured50.3%Only o1 measured
LiveCodeBench (Artificial Analysis run)23.2%67.9%o1
Terminal-Bench Hard (Artificial Analysis run)3.0%12.9%o1
WeirdML9.0%46.1%o1
LMArena Text: coding12601367o1
LMArena Text: software and IT12601366o1
Neither model has a result on the other 30 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for Llama 3.1 70B Instruct and o1
BenchmarkLlama 3.1 70B Instructo1Leader
AA Intelligence Index6.615.2o1
Epoch Capabilities Index125.9141.7o1
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Llama 3.1 70B Instruct and o1
BenchmarkLlama 3.1 70B Instructo1Leader
τ²-Bench Telecom15.2%62.6%o1
METR time horizonsNot measured51.1%Only o1 measured
BALROG27.9%Not measuredOnly Llama 3.1 70B Instruct measured
TheAgentCompany6.9%Not measuredOnly Llama 3.1 70B Instruct measured
Neither model has a result on the other 35 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for Llama 3.1 70B Instruct and o1
BenchmarkLlama 3.1 70B Instructo1Leader
GPQA DiamondNot measured73.2%Only o1 measured
MMMU Pro (Vals AI)Not measured77.4%Only o1 measured
GPQA Diamond (Artificial Analysis run)40.9%74.7%o1
Humanity's Last Exam (Artificial Analysis run)4.5%7.0%o1
GPQA Diamond (Epoch AI run)44.2%76.8%o1
ARC-AGINot measured30.7%Only o1 measured
SimpleBenchNot measured40.1%Only o1 measured
Chess puzzlesNot measured10.0%Only o1 measured
VPCTNot measured37.0%Only o1 measured
LMArena Text: hard prompts12411371o1
LMArena Text: expert12091361o1
LMArena Text: hard prompts in English12611372o1
Neither model has a result on the other 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index04
Math benchmarks for Llama 3.1 70B Instruct and o1
BenchmarkLlama 3.1 70B Instructo1Leader
AIMENot measured71.5%Only o1 measured
MATH 500 (Vals AI)Not measured90.4%Only o1 measured
MGSM (Vals AI)Not measured89.3%Only o1 measured
AA Math Index4.0Not measuredOnly Llama 3.1 70B Instruct measured
AIME 2025 (Artificial Analysis run)4.0%Not measuredOnly Llama 3.1 70B Instruct measured
OTIS Mock AIME3.6%73.3%o1
FrontierMath, tiers 1 to 3Not measured14.7%Only o1 measured
MATH level 536.7%94.7%o1
LMArena Text: math12521388o1
LMArena Text: mathematical work12531390o1
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
Knowledge benchmarks for Llama 3.1 70B Instruct and o1
BenchmarkLlama 3.1 70B Instructo1Leader
MMLU ProNot measured83.5%Only o1 measured
LegalBench (Vals AI)Not measured80.4%Only o1 measured
TaxEval (Vals AI)Not measured74.3%Only o1 measured
MedQA (Vals AI)Not measured96.5%Only o1 measured
MMLU Pro (Artificial Analysis run)67.6%84.1%o1
SimpleQA VerifiedNot measured41.1%Only o1 measured
MMLU80.1%Not measuredOnly Llama 3.1 70B Instruct measured
GeoBenchNot measured80.0%Only o1 measured
LMArena Text: science12761369o1
LMArena Text: medicine and healthcare12571355o1
LMArena Text: legal and government12831368o1
LMArena Text: business and finance12351328o1
Neither model has a result on the other 5 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for Llama 3.1 70B Instruct and o1
BenchmarkLlama 3.1 70B Instructo1Leader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
General benchmarks for Llama 3.1 70B Instruct and o1
BenchmarkLlama 3.1 70B Instructo1Leader
IFBench34.4%70.3%o1
Long context reasoning (AA-LCR)Not measured65.0%Only o1 measured
DTBench60.0%74.7%o1
LMCA14.8%22.3%o1
Fiction.LiveBenchNot measured83.3%Only o1 measured
Creative writing (Lech Mazur)Not measured70.2%Only o1 measured
LMArena Text12611366o1
LMArena Text: instruction following12311367o1
LMArena Text: creative writing12321347o1
LMArena VisionNot measured1168Only o1 measured
LMArena Text: English12941372o1
LMArena Text: non-English12191358o1
LMArena Text: Chinese12151394o1
LMArena Text: German12221337o1
LMArena Text: Japanese11321346o1
LMArena Text: Korean11401396o1
LMArena Text: French12621344o1
LMArena Text: Spanish12531345o1
LMArena Text: Russian12341356o1
LMArena Text: multi-turn12561355o1
LMArena Text: longer queries12411378o1
LMArena Text: writing and language12361360o1
LMArena Text: entertainment and media12301358o1
LMArena Vision: EnglishNot measured1162Only o1 measured
LMArena Vision: ChineseNot measured1235Only o1 measured
Neither model has a result on the other 21 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Llama 3.1 70B Instruct and o1
BenchmarkLlama 3.1 70B Instructo1Leader
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

Llama 3.1 70B Instruct$0.0008Fits in one request
o1$0.045Fits in one request

Llama 3.1 70B Instruct costs less

Repository review

50K input + 3K output tokens

Llama 3.1 70B Instruct$0.030Fits in one request
o1$0.930Fits in one request

Llama 3.1 70B Instruct costs less

Agent loop

200K input + 10K output tokens

Llama 3.1 70B Instruct$0.118Exceeds the context window
o1$3.60Exceeds the context window

Llama 3.1 70B 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 Llama 3.1 70B Instruct and o1
SpecificationMeta Llama 3.1 70B InstructOpenAI o1
MakerMeta LlamaOpenAI
WeightsOpen weightsNo public weights listed
ReleasedJuly 23, 2024December 17, 2024
Context window131K200K
Input $/Mtok$0.56$15.00
Output $/Mtok$0.56$60.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Llama 3.1 70B Instruct better than o1 for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Llama 3.1 70B Instruct for output tokens 107.1x cheaper ($0.56 vs $60.00 per million) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Which is cheaper, Llama 3.1 70B Instruct or o1?

Llama 3.1 70B Instruct costs $0.56 per million input tokens and $0.56 per million output tokens; o1 costs $15.00 and $60.00. Cost per solved task is known for neither of them.

Which has the bigger context window, Llama 3.1 70B Instruct or o1?

o1, with 200K tokens against 131K.

How many benchmarks is the Llama 3.1 70B Instruct vs o1 comparison based on?

42 benchmarks have a result for both models as of October 21, 2026. Llama 3.1 70B Instruct's coding index rests on 0 independent benchmarks and o1's on 0. Where only one model has a result, the page says so instead of naming a winner.

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