Llama 3.3 70B Instruct vs Step 3.5 Flash
Step 3.5 Flash leads Llama 3.3 70B Instruct on the Artificial Analysis Intelligence Index, 17.0 to 7.7, and Step 3.5 Flash costs 2.4x less per output token ($0.30 vs $0.72 per million). Updated October 21, 2026; 35 benchmarks have a result for both.
Llama 3.3 70B Instruct or Step 3.5 Flash: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Llama 3.3 70B Instruct for work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.
- Shared results
- 35
- Llama 3.3 70B Instruct only
- 16
- Step 3.5 Flash only
- 0
- 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Step 3.5 Flash
Step 3.5 Flash leads, 17.0 against 7.7.
Same board, both measured
Long documents
Listed context window
Step 3.5 Flash
Step 3.5 Flash takes 262K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Step 3.5 Flash
Llama 3.3 70B Instruct $0.0008, Step 3.5 Flash $0.0003 per turn.
List prices
At a glance
- Coding index
- Llama 3.3 70B Instruct not benchmarked, Step 3.5 Flash not benchmarked.
- Widest category
- Agentic work, 67.8 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: Llama 3.3 70B Instruct 26.6% against Step 3.5 Flash 94.4%, 67.8 points apart on a 0 to 100 scale.
- Output price
- Llama 3.3 70B Instruct $0.72 against Step 3.5 Flash $0.30 per million output tokens, 2.4x apart.
- Context window
- Llama 3.3 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.
| Category | Llama 3.3 70B Instruct | Bars | Step 3.5 Flash | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 0 | 19 | ||
| All shared boards | 0 | 35 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | Llama 3.3 70B Instruct | Step 3.5 Flash | Leader |
|---|---|---|---|
| AA Coding Index | 11.9 | Not measured | Only Llama 3.3 70B Instruct measured |
| LiveCodeBench (Artificial Analysis run) | 28.8% | Not measured | Only Llama 3.3 70B Instruct measured |
| Terminal-Bench Hard (Artificial Analysis run) | 3.0% | 32.6% | Step 3.5 Flash |
| WeirdML | 14.4% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: coding | 1268 | 1436 | Step 3.5 Flash |
| LMArena Text: software and IT | 1273 | 1434 | Step 3.5 Flash |
| Neither model has a result on the other 32 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.3 70B Instruct | Step 3.5 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.7 | 17.0 | Step 3.5 Flash |
| Epoch Capabilities Index | 127.3 | Not measured | Only Llama 3.3 70B Instruct measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 3.3 70B Instruct | Step 3.5 Flash | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 26.6% | 94.4% | Step 3.5 Flash |
| BALROG | 23.0% | Not measured | Only Llama 3.3 70B Instruct measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Llama 3.3 70B Instruct | Step 3.5 Flash | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 49.8% | 83.1% | Step 3.5 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 3.6% | 24.5% | Step 3.5 Flash |
| GPQA Diamond (Epoch AI run) | 47.4% | Not measured | Only Llama 3.3 70B Instruct measured |
| SimpleBench | 19.9% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: hard prompts | 1257 | 1411 | Step 3.5 Flash |
| LMArena Text: expert | 1225 | 1421 | Step 3.5 Flash |
| LMArena Text: hard prompts in English | 1277 | 1421 | Step 3.5 Flash |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Llama 3.3 70B Instruct | Step 3.5 Flash | Leader |
|---|---|---|---|
| AA Math Index | 7.7 | Not measured | Only Llama 3.3 70B Instruct measured |
| AIME 2025 (Artificial Analysis run) | 7.7% | Not measured | Only Llama 3.3 70B Instruct measured |
| OTIS Mock AIME | 5.1% | Not measured | Only Llama 3.3 70B Instruct measured |
| MATH level 5 | 41.6% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: math | 1267 | 1408 | Step 3.5 Flash |
| LMArena Text: mathematical work | 1267 | 1421 | Step 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
| Benchmark | Llama 3.3 70B Instruct | Step 3.5 Flash | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 71.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| MMLU | 86.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text: science | 1284 | 1415 | Step 3.5 Flash |
| LMArena Text: medicine and healthcare | 1275 | 1408 | Step 3.5 Flash |
| LMArena Text: legal and government | 1284 | 1408 | Step 3.5 Flash |
| LMArena Text: business and finance | 1243 | 1394 | Step 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
| Benchmark | Llama 3.3 70B Instruct | Step 3.5 Flash | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
| Benchmark | Llama 3.3 70B Instruct | Step 3.5 Flash | Leader |
|---|---|---|---|
| IFBench | 47.1% | 66.5% | Step 3.5 Flash |
| Long context reasoning (AA-LCR) | 15.7% | 63.0% | Step 3.5 Flash |
| DTBench | 59.5% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMCA | 17.5% | Not measured | Only Llama 3.3 70B Instruct measured |
| Fiction.LiveBench | 33.3% | Not measured | Only Llama 3.3 70B Instruct measured |
| LMArena Text | 1274 | 1403 | Step 3.5 Flash |
| LMArena Text: instruction following | 1242 | 1385 | Step 3.5 Flash |
| LMArena Text: creative writing | 1250 | 1357 | Step 3.5 Flash |
| LMArena Text: English | 1303 | 1417 | Step 3.5 Flash |
| LMArena Text: non-English | 1236 | 1385 | Step 3.5 Flash |
| LMArena Text: Chinese | 1217 | 1446 | Step 3.5 Flash |
| LMArena Text: German | 1251 | 1405 | Step 3.5 Flash |
| LMArena Text: Japanese | 1150 | 1353 | Step 3.5 Flash |
| LMArena Text: Korean | 1142 | 1351 | Step 3.5 Flash |
| LMArena Text: French | 1282 | 1421 | Step 3.5 Flash |
| LMArena Text: Spanish | 1269 | 1419 | Step 3.5 Flash |
| LMArena Text: Russian | 1252 | 1385 | Step 3.5 Flash |
| LMArena Text: Polish | 1259 | 1389 | Step 3.5 Flash |
| LMArena Text: multi-turn | 1280 | 1404 | Step 3.5 Flash |
| LMArena Text: longer queries | 1255 | 1402 | Step 3.5 Flash |
| LMArena Text: writing and language | 1255 | 1372 | Step 3.5 Flash |
| LMArena Text: entertainment and media | 1241 | 1360 | Step 3.5 Flash |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Llama 3.3 70B Instruct | Step 3.5 Flash | Leader |
|---|---|---|---|
| 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
Step 3.5 Flash costs less
Repository review
50K input + 3K output tokens
Step 3.5 Flash costs less
Agent loop
200K input + 10K output tokens
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.
| Specification | Meta Llama 3.3 70B Instruct | StepFun Step 3.5 Flash |
|---|---|---|
| Maker | Meta Llama | StepFun |
| Weights | Open weights | Open weights |
| Released | December 6, 2024 | January 29, 2026 |
| Context window | 131K | 262K |
| Input $/Mtok | $0.45 | $0.10 |
| Output $/Mtok | $0.72 | $0.30 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Llama 3.3 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. Pick Llama 3.3 70B Instruct for work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.
Which is cheaper, Llama 3.3 70B Instruct or Step 3.5 Flash?
Llama 3.3 70B Instruct costs $0.45 per million input tokens and $0.72 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 3.3 70B Instruct or Step 3.5 Flash?
Step 3.5 Flash, with 262K tokens against 131K.
How many benchmarks is the Llama 3.3 70B Instruct vs Step 3.5 Flash comparison based on?
35 benchmarks have a result for both models as of October 21, 2026. Llama 3.3 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.