text model comparison
GPT-6.1 Terra vs Mistral Large 4
GPT-6.1 Terra and Mistral Large 4 are both current text models, from OpenAI and Mistral AI. Mistral Large 4 is 30% cheaper per 1M input tokens than GPT-6.1 Terra, $0.70 vs $1. On Arena WebDev, GPT-6.1 Terra scores 1673 Elo and Mistral Large 4 1544 Elo (Arena, measured October 28, 2026). GPT-6.1 Terra has the larger context window, 1,050,000 vs 1,000,000 tokens.
- Higher Arena WebDev
- GPT-6.1 Terra1673 Elo against 1544 Elo
- Lower price per 1M input tokens
- Mistral Large 4$0.70 against $1
- Longer context window
- GPT-6.1 Terra1.05M tokens against 1M tokens
- Newer release
- GPT-6.1 TerraOct 2026 against Oct 2026
Data verified November 3, 2026 · Dataset 2026-11-03
Side by side
GPT-6.1 Terra and Mistral Large 4, line by line
Prices in effect today, compared at the same tier wherever both list one. The better value in each row is highlighted.
| Fact | GPT-6.1 Terra | Mistral Large 4 |
|---|---|---|
| Price | ||
| Price per 1M input tokens | $1 | $0.70 (better) |
| Price per 1M output tokens | $5 | $2.10 (better) |
| Independent benchmarks | ||
| Arena WebDev | 1673 Elo (#10 of 45) (better) | 1544 Elo (#32 of 45) |
| FrontierMath Tiers 1-3 (v2) | 90.9% (#4 of 42) (better) | 46.3% (#36 of 42) |
| GPQA Diamond | 94.3% (#7 of 41) (better) | 90.2% (#28 of 41) |
| SimpleQA Verified | 57.3% (#15 of 38) (better) | 22.4% (#36 of 38) |
| Arena Text | Not measured | 1432 Elo (#40 of 51) |
| Specs | ||
| Provider | OpenAI | Mistral AI |
| Release date | October 26, 2026 | October 6, 2026 |
| Access | API only | API and open weights |
| Context window | 1,050,000 tokens | 1,000,000 tokens |
| Max output | 128,000 tokens | Not reported |
| Input | text, image | text, image |
| Output | text | text |
Charts
Against every current text model
Where GPT-6.1 Terra and Mistral Large 4 land among the current models of their task, on every independent benchmark, on price and on context.
Where each stands
On Arena WebDev, GPT-6.1 Terra ranks #10 and Mistral Large 4 #32 of 45 current models. Each bar is the share of current models it beats: on score, price and context.
Independent benchmarks
Arena Text
GPT-6.1 Terra: Not scored
Mistral Large 4:#40of 51
1,432 Elo · ahead of 22%
Arena WebDev
GPT-6.1 Terra:#10of 45
1,673 Elo · ahead of 80%
Mistral Large 4:#32of 45
1,544 Elo · ahead of 30%
FrontierMath Tiers 1-3 (v2)
GPT-6.1 Terra:#4of 42
90.9% · ahead of 93%
Mistral Large 4:#36of 42
46.3% · ahead of 15%
GPQA Diamond
GPT-6.1 Terra:#7of 41
94.3% · ahead of 83%
Mistral Large 4:#28of 41
90.2% · ahead of 30%
SimpleQA Verified
GPT-6.1 Terra:#15of 38
57.3% · ahead of 62%
Mistral Large 4:#36of 38
22.4% · ahead of 5%
Price and context
Price per 1M input tokens
GPT-6.1 Terra:#21of 52
$1 · cheaper than 59%
Mistral Large 4:#19of 52
$0.70 · cheaper than 65%
Price per 1M output tokens
GPT-6.1 Terra:#27of 52
$5 · cheaper than 49%
Mistral Large 4:#16of 52
$2.10 · cheaper than 71%
Context window
GPT-6.1 Terra:#1of 64
1.05M tokens · longer than 86%
Mistral Large 4:#26of 64
1M tokens · longer than 22%
Quality against price
Mistral Large 4 scores 1,432 at $1.05 (blended price per 1M tokens). The line joins the current models no other beats on both price and Arena Text score; hover any point to name it.
Quality against price: the data
| Model | Provider | Blended price per 1M tokens | Arena Text (Elo) |
|---|---|---|---|
| Gemini 4 Argon | $4 | 1,520 | |
| Claude Opus 5.5 | Anthropic | $8 | 1,509 |
| Muse Wonder | Meta | $8 | 1,507 |
| Claude Fable 5 | Anthropic | $20 | 1,504 |
| Claude Fable 5.1 | Anthropic | $20 | 1,504 |
| Gemini 3.8 Flash | $1.50 | 1,496 | |
| Muse Spark 1.3 | Meta | $2 | 1,495 |
| Claude Opus 5 | Anthropic | $10 | 1,492 |
| Kimi K3 | Moonshot Kimi | $6 | 1,488 |
| GPT-5.6 Sol | OpenAI | $8 | 1,485 |
| Gemini 3.6 Flash | $3 | 1,483 | |
| GPT-5.5 | OpenAI | $11 | 1,482 |
| Claude Opus 4.8 | Anthropic | $10 | 1,482 |
| Qwen3.8-Max | Alibaba Qwen | $3 | 1,482 |
| GPT-6.1 Sol | OpenAI | $4 | 1,481 |
| GLM-5.3 | Z.ai | $2.15 | 1,481 |
| MiMo-V2.6-Pro | Xiaomi MiMo | $0.54 | 1,480 |
| Claude Sonnet 5.5 | Anthropic | $4 | 1,477 |
| GLM-5.3-Flash | Z.ai | $0.24 | 1,477 |
| GPT-6 Astra | OpenAI | $20 | 1,476 |
| DeepSeek-V4.1-Flash | DeepSeek | $0.52 | 1,476 |
| GPT-5.4 | OpenAI | $5.63 | 1,475 |
| Gemini 3 Flash Preview | $1.13 | 1,473 | |
| Grok 4.20 | xAI | $1.56 | 1,472 |
| GPT-5.6 Terra | OpenAI | $4.50 | 1,467 |
| Grok 4.5 | xAI | $3 | 1,465 |
| DeepSeek-V4-Pro | DeepSeek | $1.98 | 1,465 |
| Claude Sonnet 5 | Anthropic | $4 | 1,462 |
| GPT-6 Sol | OpenAI | $4 | 1,458 |
| Gemini 3.5 Flash-Lite | $0.85 | 1,456 | |
| Grok 4.6 | xAI | $3 | 1,455 |
| GPT-5.6 Luna | OpenAI | $0.45 | 1,453 |
| MiMo-V2.6-Flash | Xiaomi MiMo | $0.18 | 1,448 |
| Step 5 Preview | StepFun | $1.43 | 1,448 |
| Grok 4.3 | xAI | $1.56 | 1,442 |
| GPT-6 Luna | OpenAI | $0.20 | 1,441 |
| Grok 4.7 | xAI | $3 | 1,441 |
| Mistral Large 4 | Mistral AI | $1.05 | 1,432 |
| Claude Haiku 5.5 | Anthropic | $0.20 | 1,425 |
| GPT-4.1 | OpenAI | $3.50 | 1,415 |
| Solar Pro 4 | Upstage | $0.52 | 1,388 |
| GPT-4.1 mini | OpenAI | $0.70 | 1,383 |
| GPT-4.1 nano | OpenAI | $0.18 | 1,322 |
Cost
What each costs
Example workloads at each provider's listed rates, then every other rate either one lists. The cheaper figure in each row is highlighted.
The same work, priced
For 10M input and 2M output tokens, GPT-6.1 Terra costs 1.8 times as much as Mistral Large 4, at each provider's listed rates.
10M input and 2M output tokens
GPT-6.1 Terra costs 1.8× as much
- GPT-6.1 Terra
- $20
- Mistral Large 4
- $11.20
The same, with 9M of the input tokens cached
GPT-6.1 Terra costs 2.1× as much
- GPT-6.1 Terra
- $11.45
- Mistral Large 4
- $5.53
| Workload or rate | GPT-6.1 Terra | Mistral Large 4 |
|---|---|---|
| Example workloads | ||
| 10M input and 2M output tokens | $20 | $11.20 (better) |
| The same, with 9M of the input tokens cached | $11.45 | $5.53 (better) |
| Other listed rates | ||
| 1M input tokens, cached | $0.05 (better) | $0.07 |
| 1M input tokens, prompts over 272K tokens | $2 | Not listed |
| 1M output tokens, prompts over 272K tokens | $7.50 | Not listed |
Your own bill will differ. These figures use list prices and example workloads. The AI Model Cost Audit reviews your real usage, shortlists models that score as well for each task for less, and estimates the savings.
Launch price $100, regular price $150
Verdict
When to choose each
GPT-6.1 Terra
Where it leads
- Higher Arena WebDev score, 1673 Elo vs 1544 Elo
- Higher FrontierMath Tiers 1-3 (v2) score, 90.9% vs 46.3%
- Higher GPQA Diamond score, 94.3% vs 90.2%
- Higher SimpleQA Verified score, 57.3% vs 22.4%
- Larger context window 1,050,000 vs 1,000,000 tokens
- Cheaper cached input, $0.05 vs $0.07 per 1M tokens
Best for
- Computer-use and tool-calling agents at half GPT-6.1 Sol's price
- High-volume reasoning work that stays under the 272K long-context threshold
Watch out for
- Prompts over 272,000 input tokens bill the whole request at $2 input and $7.50 output per 1M tokens
- Reads text and images only: audio and video input are not supported
Mistral Large 4
Where it leads
- Lower input and output prices, $0.70 and $2.10 vs $1 and $5 per 1M tokens
- Open weights you can run yourself
Best for
- Self-hosted or European data-residency deployments that want a frontier-scale multimodal model
- Coding and security agents where open weights and a low API price both matter
Watch out for
- The API model is still labelled a public preview, and the open weights are a further-trained build of the October 6 preview
- Businesses that sell it as a hosted model service need a separate commercial licence from Mistral
- Running the open weights moves cost to your own GPUs, which the token rates here do not represent
- Epoch AI measured 22.4% on SimpleQA Verified for the open-weight build, low for its size, so check factual recall on your own questions
From benchmark to production
Building on GPT-6.1 Terra or Mistral Large 4?
Public scores rarely hold on your own data. We test both models on your real tasks, then build the agent or pipeline around the one that wins.
AI Model Cost AuditLaunch price $100, regular price $150What the audit includes
FAQ
GPT-6.1 Terra vs Mistral Large 4 FAQ
Which is cheaper, GPT-6.1 Terra or Mistral Large 4?
Mistral Large 4 is 30% cheaper per 1M input tokens than GPT-6.1 Terra, $0.70 vs $1. For 10M input and 2M output tokens, GPT-6.1 Terra costs $20 and Mistral Large 4 $11.20 at standard rates.
Which is cheaper with prompt caching, GPT-6.1 Terra or Mistral Large 4?
GPT-6.1 Terra charges $0.05 per 1M cached input tokens, 95% below its standard input rate, and Mistral Large 4 $0.07, 90% below. With 9M of 10M input tokens cached and 2M output tokens, GPT-6.1 Terra costs $11.45 and Mistral Large 4 $5.53.
Which scores higher on independent benchmarks, GPT-6.1 Terra or Mistral Large 4?
GPT-6.1 Terra leads on Arena WebDev, 1673 Elo to 1544 Elo; GPT-6.1 Terra leads on FrontierMath Tiers 1-3 (v2), 90.9% to 46.3%; GPT-6.1 Terra leads on GPQA Diamond, 94.3% to 90.2%. Scores are independent evaluations, not either vendor's launch figures.
Which has the larger context window, GPT-6.1 Terra or Mistral Large 4?
GPT-6.1 Terra has the larger context window, 1,050,000 vs 1,000,000 tokens.
Can I run GPT-6.1 Terra or Mistral Large 4 myself?
Mistral Large 4 is released as open weights you can run yourself; GPT-6.1 Terra is available through its provider's API only.
Further reading
More on GPT-6.1 Terra and Mistral Large 4
Articles on GPT-6.1 Terra and Mistral Large 4
- GPT-6.1 Terra: Price, Benchmarks and When to Use ItOctober 27, 2026
Related comparisons
- Gemini 3.8 Flash vs GPT-6.1 Terra
- Claude Haiku 5.5 vs Mistral Large 4
- GPT-6.1 Sol vs GPT-6.1 Terra
- DeepSeek-V4-Pro vs Mistral Large 4
- GPT-6.1 Terra vs Grok 4.20
- Gemini 3.6 Flash vs Mistral Large 4
- GPT-6.1 Terra vs Grok 4.3
- Gemini 3.8 Flash vs Mistral Large 4
- GPT-5.6 Terra vs GPT-6.1 Terra
- Gemini 3 Flash Preview vs Mistral Large 4
- GPT-6.1 Terra vs Muse Spark 1.3
- GLM-5.3 vs Mistral Large 4
Production evaluation
Choosing between GPT-6.1 Terra and Mistral Large 4?
Public benchmarks narrow the shortlist. We run both on your own tasks, data and cost limits, then build the routing and review controls to run the winner in production.
- A head-to-head evaluation on your real workload
- Routing between models by cost and difficulty