Cheaper alternatives to Llama 3.1 70B Instruct
Meta LMArena 1261 $0.400/M on a balanced workload prices as of 2026-08-28
50 models are both better and cheaper, giving up nothing.
Of the 133 models carrying both an independent score and a published price at the same delivery mode, 56 score at least as high as Llama 3.1 70B Instruct and cost no more under at least one workload. 50 match or beat it on every capability we hold — context, maximum output, input modes, tool use and extended reasoning. 6 do not, and every row names what it drops.
Dominance is not a property of a model. It is a property of a model, a capability lens and a workload, and all three are named on every row. Scores are LMArena Elo, used under CC BY 4.0 from the official dataset; prices are blended per million tokens. A higher score is not a drop-in replacement.
This page is built from that comparison and nothing else. When no model is both better and cheaper than Llama 3.1 70B Instruct, it is not generated.
Where it holds
| Workload | Llama 3.1 70B Instruct $/M | Better and cheaper | With a trade |
|---|---|---|---|
| Balanced | $0.400 | 25 | 5 |
| Summarise | $0.400 | 50 | 6 |
| Chat | $0.400 | 23 | 5 |
| Code gen | $0.400 | 22 | 3 |
| Agentic | $0.400 | 44 | 6 |
Workload mixes are defined on the methodology page. Cached input is priced at the cached rate, and reasoning tokens at the worse of the reasoning and output rates.
What you would be replacing
| Intelligence | 1261 |
|---|---|
| Context window | 131K |
| Max output | 16K |
| Input modes | text |
| Tool use | yes |
| Extended reasoning | no |
A replacement has to clear every line above, not just the score. Full record for Llama 3.1 70B Instruct.
Better and cheaper, nothing given up 50
Each of these matches or beats Llama 3.1 70B Instruct on context, maximum output, input modes, tool use and reasoning, scores at least as high, and costs no more.
| # | Model | Intelligence | $/M | Holds under |
|---|---|---|---|---|
| 1 | Gemma 4 31B Google | 1441.7 +180.7 | $0.152 |
|
| 2 | Hy3 Tencent | 1441.2 +180.2 | $0.144 |
|
| 3 | Gemma 4 26B A4B Google | 1434.6 +173.6 | $0.138 |
|
| 4 | DeepSeek V4 Flash 0423 DeepSeek | 1431.6 +170.6 | $0.101 |
|
| 5 | MiMo-V2.5 Xiaomi | 1427.3 +166.3 | $0.175 |
|
| 6 | DeepSeek V3.2 DeepSeek | 1424.6 +163.6 | $0.290 |
|
| 7 | DeepSeek V3.2 Exp DeepSeek | 1424.4 +163.4 | $0.305 |
|
| 8 | Qwen3 235B A22B Instruct 2507 Qwen | 1419.3 +158.3 | $0.205 |
|
| 9 | Qwen3 Next 80B A3B Instruct Qwen | 1418.6 +157.6 | $0.350 |
|
| 10 | Step 3.5 Flash StepFun | 1403.8 +142.8 | $0.150 |
|
| 11 | Qwen3.5-Flash Qwen | 1397.6 +136.6 | $0.114 |
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| 12 | Qwen3 30B A3B Instruct 2507 Qwen | 1384.3 +123.3 | $0.084 |
|
| 13 | GLM 4.5 Air Z.ai | 1382.8 +121.8 | $0.310 |
|
| 14 | Solar Pro 4 Upstage | 1376.2 +115.2 | $0.052 |
|
| 15 | gpt-oss-120b OpenAI | 1365.6 +104.6 | $0.070 |
|
| 16 | Gemma 3 27B Google | 1358.3 +97.3 | $0.172 |
|
| 17 | GLM 4.7 Flash Z.ai | 1352.9 +91.9 | $0.145 |
|
| 18 | Qwen3 32B Qwen | 1340.1 +79.1 | $0.130 |
|
| 19 | Trinity Large Thinking Arcee | 1341.9 +80.9 | $0.378 |
|
| 20 | Gemma 3 12B Google | 1334.2 +73.2 | $0.075 |
|
| 21 | GPT-5 Nano OpenAI | 1320.3 +59.3 | $0.138 |
|
| 22 | Qwen3 30B A3B Qwen | 1316.9 +55.9 | $0.215 |
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| 23 | Granite 4.1 8B IBM | 1291.6 +30.6 | $0.063 |
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| 24 | gpt-oss-20b OpenAI | 1287.8 +26.8 | $0.055 |
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| 25 | GPT-4.1 Nano OpenAI | 1284.8 +23.8 | $0.175 |
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| 26 | Gemini 3.7 Flash Google | 1490.2 +229.2 | $0.354 summarise |
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| 27 | MiMo-V2.5-Pro Xiaomi | 1465 +204 | $0.334 summarise |
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| 28 | Qwen3.7 Plus Qwen | 1456.2 +195.2 | $0.295 summarise |
|
| 29 | Gemini 3.5 Flash Lite Google | 1436.5 +175.5 | $0.333 summarise |
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| 30 | MiniMax M3 MiniMax | 1434.8 +173.8 | $0.277 summarise |
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| 31 | GLM 4.7 Z.ai | 1435.3 +174.3 | $0.376 summarise |
|
| 32 | GPT-5.6 Luna OpenAI | 1428.5 +167.5 | $0.199 summarise |
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| 33 | Qwen3 VL 235B A22B Instruct Qwen | 1420.9 +159.9 | $0.263 summarise |
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| 34 | DeepSeek V3.1 Terminus DeepSeek | 1419.6 +158.6 | $0.268 summarise |
|
| 35 | Qwen3.5-122B-A10B Qwen | 1417.9 +156.9 | $0.351 summarise |
|
| 36 | Gemini 2.5 Flash Google | 1417.3 +156.3 | $0.333 summarise |
|
| 37 | Gemini 3.1 Flash Lite Preview Google | 1414.8 +153.8 | $0.248 summarise |
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| 38 | Qwen3 235B A22B Thinking 2507 Qwen | 1413.8 +152.8 | $0.334 summarise |
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| 39 | Inkling Small Thinking Machines | 1411.7 +150.7 | $0.388 summarise |
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| 40 | Qwen3.5-27B Qwen | 1407.9 +146.9 | $0.263 summarise |
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| 41 | MiniMax M2.7 MiniMax | 1405.3 +144.3 | $0.277 summarise |
|
| 42 | Qwen3.5-35B-A3B Qwen | 1395.6 +134.6 | $0.300 summarise |
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| 43 | GLM 4.6V Z.ai | 1374.7 +113.7 | $0.260 summarise |
|
| 44 | DeepSeek V3 0324 DeepSeek | 1375 +114 | $0.287 summarise |
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| 45 | GPT-5.4 Nano OpenAI | 1372.8 +111.8 | $0.201 summarise |
|
| 46 | GPT-5 Mini OpenAI | 1373.4 +112.4 | $0.273 summarise |
|
| 47 | Qwen3 Next 80B A3B Thinking Qwen | 1367.5 +106.5 | $0.203 summarise |
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| 48 | MiniMax M2.5 MiniMax | 1359 +98 | $0.241 summarise |
|
| 49 | MiniMax M2 MiniMax | 1342.1 +81.1 | $0.293 summarise |
|
| 50 | GPT-4.1 Mini OpenAI | 1340.5 +79.5 | $0.374 summarise |
|
Cheaper and higher-scoring, but you give something up 6
These score at least as high and cost no more on the two plotted axes, and lose something that is not on them. Read the last column before switching.
| Model | Intelligence | $/M | Holds under | What you give up |
|---|---|---|---|---|
| Mercury 2 Inception | 1357.8 +96.8 | $0.375 |
|
|
| Olmo 3 32B Think Allen AI | 1298.5 +37.5 | $0.237 |
|
|
| Gemma 3 4B Google | 1290.8 +29.8 | $0.063 |
|
|
| GPT-4o-mini (2024-07-18) OpenAI | 1286.6 +25.6 | $0.263 |
|
|
| Qwen2.5 72B Instruct Qwen | 1269.1 tie | $0.370 |
|
|
| DeepSeek V3 DeepSeek | 1332.6 +71.6 | $0.296 summarise |
|
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What this compares, and what it leaves out
- Quality is LMArena Elo, used under CC BY 4.0 from the official dataset. The 95% confidence interval on a difference between two scores is about ±10.68 points, so a gap smaller than that is marked tie rather than an improvement — see significance bands.
- 188 further models at this delivery mode carry a price but no independent score. They are absent from the comparison in both directions — unrated is not a zero, and an unmeasured model is neither an alternative nor a worse buy.
- Retired models are never offered as an alternative, and a model only competes against its own delivery mode: batch trades latency for price, so it is not a like-for-like swap.
- Nothing here measures latency, throughput, rate limits or how a model behaves on your prompts. Two models with the same index score are not interchangeable.
- The models that nothing beats on both axes are on the value frontier, and every other model something cheaper beats is listed here.