Gemma 2 27B vs Llama 3.1 70B Instruct
Gemma 2 27B and Llama 3.1 70B Instruct cost about the same per output token ($0.65 vs $0.56 per million). Updated October 21, 2026; 34 benchmarks have a result for both.
Gemma 2 27B or Llama 3.1 70B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Gemma 2 27B for work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
- Shared results
- 34
- Gemma 2 27B only
- 2
- Llama 3.1 70B Instruct only
- 13
- 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Llama 3.1 70B Instruct has a AA Intelligence Index result, 6.6.
Directional only
Long documents
Listed context window
Llama 3.1 70B Instruct
Llama 3.1 70B Instruct takes 131K tokens against 8K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 3.1 70B Instruct
Gemma 2 27B $0.0010, Llama 3.1 70B Instruct $0.0008 per turn.
List prices
At a glance
- Coding index
- Gemma 2 27B not benchmarked, Llama 3.1 70B Instruct not benchmarked.
- Widest category
- Reasoning, 7.6 points apart on average over 4 shared benchmarks.
- Widest single gap
- DTBench: Gemma 2 27B 48.0% against Llama 3.1 70B Instruct 60.0%, 12.0 points apart on a 0 to 100 scale.
- Output price
- Gemma 2 27B $0.65 against Llama 3.1 70B Instruct $0.56 per million output tokens, 1.2x apart.
- Context window
- Gemma 2 27B takes 8K tokens, Llama 3.1 70B Instruct 131K.
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 | Gemma 2 27B | Bars | Llama 3.1 70B Instruct | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 4 | ||
| Math | 0 | 4 | ||
| Knowledge | 0 | 5 | ||
| General | 6 | 12 | ||
| All shared boards | 6 | 28 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | Gemma 2 27B | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | Not measured | 23.2% | Only Llama 3.1 70B Instruct measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 3.0% | Only Llama 3.1 70B Instruct measured |
| WeirdML | Not measured | 9.0% | Only Llama 3.1 70B Instruct measured |
| LMArena Text: coding | 1211 | 1260 | Llama 3.1 70B Instruct |
| LMArena Text: software and IT | 1228 | 1260 | Llama 3.1 70B Instruct |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Gemma 2 27B | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 6.6 | Only Llama 3.1 70B Instruct measured |
| Epoch Capabilities Index | 122.1 | 125.9 | Llama 3.1 70B Instruct |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | Gemma 2 27B | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 15.2% | Only Llama 3.1 70B Instruct measured |
| BALROG | Not measured | 27.9% | Only Llama 3.1 70B Instruct measured |
| TheAgentCompany | Not measured | 6.9% | Only Llama 3.1 70B Instruct measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Gemma 2 27B | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 40.9% | Only Llama 3.1 70B Instruct measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 4.5% | Only Llama 3.1 70B Instruct measured |
| GPQA Diamond (Epoch AI run) | 36.5% | 44.2% | Llama 3.1 70B Instruct |
| LMArena Text: hard prompts | 1198 | 1241 | Llama 3.1 70B Instruct |
| LMArena Text: expert | 1171 | 1209 | Llama 3.1 70B Instruct |
| LMArena Text: hard prompts in English | 1206 | 1261 | Llama 3.1 70B Instruct |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | Gemma 2 27B | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 4.0 | Only Llama 3.1 70B Instruct measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 4.0% | Only Llama 3.1 70B Instruct measured |
| OTIS Mock AIME | 1.4% | 3.6% | Llama 3.1 70B Instruct |
| MATH level 5 | 27.9% | 36.7% | Llama 3.1 70B Instruct |
| LMArena Text: math | 1212 | 1252 | Llama 3.1 70B Instruct |
| LMArena Text: mathematical work | 1221 | 1253 | Llama 3.1 70B Instruct |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | Gemma 2 27B | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 68.0% | Not measured | Only Gemma 2 27B measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 67.6% | Only Llama 3.1 70B Instruct measured |
| MMLU | 75.7% | 80.1% | Llama 3.1 70B Instruct |
| LMArena Text: science | 1228 | 1276 | Llama 3.1 70B Instruct |
| LMArena Text: medicine and healthcare | 1198 | 1257 | Llama 3.1 70B Instruct |
| LMArena Text: legal and government | 1241 | 1283 | Llama 3.1 70B Instruct |
| LMArena Text: business and finance | 1208 | 1235 | Llama 3.1 70B Instruct |
| 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 | Gemma 2 27B | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index612
| Benchmark | Gemma 2 27B | Llama 3.1 70B Instruct | Leader |
|---|---|---|---|
| IFBench | Not measured | 34.4% | Only Llama 3.1 70B Instruct measured |
| DTBench | 48.0% | 60.0% | Llama 3.1 70B Instruct |
| LMCA | 7.1% | 14.8% | Llama 3.1 70B Instruct |
| LMArena Text | 1231 | 1261 | Llama 3.1 70B Instruct |
| LMArena Text: instruction following | 1206 | 1231 | Llama 3.1 70B Instruct |
| LMArena Text: creative writing | 1240 | 1232 | Gemma 2 27B |
| LMArena Text: English | 1242 | 1294 | Llama 3.1 70B Instruct |
| LMArena Text: non-English | 1217 | 1219 | Llama 3.1 70B Instruct |
| LMArena Text: Chinese | 1221 | 1215 | Gemma 2 27B |
| LMArena Text: German | 1209 | 1222 | Llama 3.1 70B Instruct |
| LMArena Text: Japanese | 1175 | 1132 | Gemma 2 27B |
| LMArena Text: Korean | 1174 | 1140 | Gemma 2 27B |
| LMArena Text: French | 1248 | 1262 | Llama 3.1 70B Instruct |
| LMArena Text: Spanish | 1228 | 1253 | Llama 3.1 70B Instruct |
| LMArena Text: Russian | 1234 | 1234 | Gemma 2 27B |
| LMArena Text: Polish | 1277 | Not measured | Only Gemma 2 27B measured |
| LMArena Text: multi-turn | 1224 | 1256 | Llama 3.1 70B Instruct |
| LMArena Text: longer queries | 1230 | 1241 | Llama 3.1 70B Instruct |
| LMArena Text: writing and language | 1249 | 1236 | Gemma 2 27B |
| LMArena Text: entertainment and media | 1212 | 1230 | Llama 3.1 70B Instruct |
| Neither model has a result on the other 26 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemma 2 27B | Llama 3.1 70B Instruct | 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
Llama 3.1 70B Instruct costs less
Repository review
50K input + 3K output tokens
Llama 3.1 70B Instruct costs less
Agent loop
200K input + 10K output tokens
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.
| Specification | Google Gemma 2 27B | Meta Llama 3.1 70B Instruct |
|---|---|---|
| Maker | Meta Llama | |
| Weights | Open weights | Open weights |
| Released | July 13, 2024 | July 23, 2024 |
| Context window | 8K | 131K |
| Input $/Mtok | $0.65 | $0.56 |
| Output $/Mtok | $0.65 | $0.56 |
| 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 Gemma 2 27B better than Llama 3.1 70B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Gemma 2 27B for work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.
Which is cheaper, Gemma 2 27B or Llama 3.1 70B Instruct?
Gemma 2 27B costs $0.65 per million input tokens and $0.65 per million output tokens; Llama 3.1 70B Instruct costs $0.56 and $0.56. Cost per solved task is known for neither of them.
Which has the bigger context window, Gemma 2 27B or Llama 3.1 70B Instruct?
Llama 3.1 70B Instruct, with 131K tokens against 8K.
How many benchmarks is the Gemma 2 27B vs Llama 3.1 70B Instruct comparison based on?
34 benchmarks have a result for both models as of October 21, 2026. Gemma 2 27B's coding index rests on 0 independent benchmarks and Llama 3.1 70B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.