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Llama 3.1 70B Instruct vs Step 3.5 Flash

Updated October 21, 2026. 33 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Llama 3.1 70B InstructMeta Llama
Coding
No data
Overall
6.6
Output $/M
$0.56
0
Step 3.5 FlashStepFun
Coding
No data
Overall
17.0
Output $/M
$0.30
33
vs boards won of 33 shared

Decision reading

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

Llama 3.1 70B Instruct has the edge where what matters is Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Shared results
33
Llama 3.1 70B Instruct only
14
Step 3.5 Flash only
2
Like-for-like categories
7 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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, Step 3.5 Flash not benchmarked.
Widest category
Agentic work, 79.2 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: Llama 3.1 70B Instruct 15.2% against Step 3.5 Flash 94.4%, 79.2 points apart on a 0 to 100 scale.
Output price
Llama 3.1 70B Instruct $0.56 against Step 3.5 Flash $0.30 per million output tokens, 1.9x apart.
Context window
Llama 3.1 70B Instruct takes 131K tokens, Step 3.5 Flash 262K.

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 InstructBarsStep 3.5 FlashTies
Coding03
Intelligence01
Agentic work01
Reasoning05
Math02
Knowledge04
General017
All shared boards033
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for Llama 3.1 70B Instruct and Step 3.5 Flash
BenchmarkLlama 3.1 70B InstructStep 3.5 FlashLeader
LiveCodeBench (Artificial Analysis run)23.2%Not measuredOnly Llama 3.1 70B Instruct measured
Terminal-Bench Hard (Artificial Analysis run)3.0%32.6%Step 3.5 Flash
WeirdML9.0%Not measuredOnly Llama 3.1 70B Instruct measured
LMArena Text: coding12601436Step 3.5 Flash
LMArena Text: software and IT12601434Step 3.5 Flash
Neither model has a result on the other 33 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Llama 3.1 70B Instruct and Step 3.5 Flash
BenchmarkLlama 3.1 70B InstructStep 3.5 FlashLeader
AA Intelligence Index6.617.0Step 3.5 Flash
Epoch Capabilities Index125.9Not measuredOnly Llama 3.1 70B Instruct measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Llama 3.1 70B Instruct and Step 3.5 Flash
BenchmarkLlama 3.1 70B InstructStep 3.5 FlashLeader
τ²-Bench Telecom15.2%94.4%Step 3.5 Flash
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 36 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for Llama 3.1 70B Instruct and Step 3.5 Flash
BenchmarkLlama 3.1 70B InstructStep 3.5 FlashLeader
GPQA Diamond (Artificial Analysis run)40.9%83.1%Step 3.5 Flash
Humanity's Last Exam (Artificial Analysis run)4.5%24.5%Step 3.5 Flash
GPQA Diamond (Epoch AI run)44.2%Not measuredOnly Llama 3.1 70B Instruct measured
LMArena Text: hard prompts12411411Step 3.5 Flash
LMArena Text: expert12091421Step 3.5 Flash
LMArena Text: hard prompts in English12611421Step 3.5 Flash
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Llama 3.1 70B Instruct and Step 3.5 Flash
BenchmarkLlama 3.1 70B InstructStep 3.5 FlashLeader
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%Not measuredOnly Llama 3.1 70B Instruct measured
MATH level 536.7%Not measuredOnly Llama 3.1 70B Instruct measured
LMArena Text: math12521408Step 3.5 Flash
LMArena Text: mathematical work12531421Step 3.5 Flash
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Llama 3.1 70B Instruct and Step 3.5 Flash
BenchmarkLlama 3.1 70B InstructStep 3.5 FlashLeader
MMLU Pro (Artificial Analysis run)67.6%Not measuredOnly Llama 3.1 70B Instruct measured
MMLU80.1%Not measuredOnly Llama 3.1 70B Instruct measured
LMArena Text: science12761415Step 3.5 Flash
LMArena Text: medicine and healthcare12571408Step 3.5 Flash
LMArena Text: legal and government12831408Step 3.5 Flash
LMArena Text: business and finance12351394Step 3.5 Flash
Neither model has a result on the other 11 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 Step 3.5 Flash
BenchmarkLlama 3.1 70B InstructStep 3.5 FlashLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index017
General benchmarks for Llama 3.1 70B Instruct and Step 3.5 Flash
BenchmarkLlama 3.1 70B InstructStep 3.5 FlashLeader
IFBench34.4%66.5%Step 3.5 Flash
Long context reasoning (AA-LCR)Not measured63.0%Only Step 3.5 Flash measured
DTBench60.0%Not measuredOnly Llama 3.1 70B Instruct measured
LMCA14.8%Not measuredOnly Llama 3.1 70B Instruct measured
LMArena Text12611403Step 3.5 Flash
LMArena Text: instruction following12311385Step 3.5 Flash
LMArena Text: creative writing12321357Step 3.5 Flash
LMArena Text: English12941417Step 3.5 Flash
LMArena Text: non-English12191385Step 3.5 Flash
LMArena Text: Chinese12151446Step 3.5 Flash
LMArena Text: German12221405Step 3.5 Flash
LMArena Text: Japanese11321353Step 3.5 Flash
LMArena Text: Korean11401351Step 3.5 Flash
LMArena Text: French12621421Step 3.5 Flash
LMArena Text: Spanish12531419Step 3.5 Flash
LMArena Text: Russian12341385Step 3.5 Flash
LMArena Text: PolishNot measured1389Only Step 3.5 Flash measured
LMArena Text: multi-turn12561404Step 3.5 Flash
LMArena Text: longer queries12411402Step 3.5 Flash
LMArena Text: writing and language12361372Step 3.5 Flash
LMArena Text: entertainment and media12301360Step 3.5 Flash
Neither model has a result on the other 25 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 Step 3.5 Flash
BenchmarkLlama 3.1 70B InstructStep 3.5 FlashLeader
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
Step 3.5 Flash$0.0003Fits in one request

Step 3.5 Flash costs less

Repository review

50K input + 3K output tokens

Llama 3.1 70B Instruct$0.030Fits in one request
Step 3.5 Flash$0.0059Fits in one request

Step 3.5 Flash costs less

Agent loop

200K input + 10K output tokens

Llama 3.1 70B Instruct$0.118Exceeds the context window
Step 3.5 Flash$0.023Fits in one request

Step 3.5 Flash 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 Step 3.5 Flash
SpecificationMeta Llama 3.1 70B InstructStepFun Step 3.5 Flash
MakerMeta LlamaStepFun
WeightsOpen weightsOpen weights
ReleasedJuly 23, 2024January 29, 2026
Context window131K262K
Input $/Mtok$0.56$0.10
Output $/Mtok$0.56$0.30
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 Step 3.5 Flash for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Llama 3.1 70B Instruct has the edge where what matters is Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Which is cheaper, Llama 3.1 70B Instruct or Step 3.5 Flash?

Llama 3.1 70B Instruct costs $0.56 per million input tokens and $0.56 per million output tokens; Step 3.5 Flash costs $0.10 and $0.30. Cost per solved task is known for neither of them.

Which has the bigger context window, Llama 3.1 70B Instruct or Step 3.5 Flash?

Step 3.5 Flash, with 262K tokens against 131K.

How many benchmarks is the Llama 3.1 70B Instruct vs Step 3.5 Flash comparison based on?

33 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 Step 3.5 Flash's on 0. Where only one model has a result, the page says so instead of naming a winner.

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