| Signal | Jamba Large 1.7 | Delta | Codestral 2508 |
|---|---|---|---|
Capabilities | 50 | -- | |
Pricing | 92 | -7 | |
Context window size | 86 | -- | |
Recency | 83 | +1 | |
Output Capacity | 60 | +40 | |
| Overall Result | 2 wins | of 5 | 1 wins |
Score History
40
current score
Tied
right now
40
current score
AI21 Labs
Mistral AI
Codestral 2508 saves you $525.00/month
That's $6300.00/year compared to Jamba Large 1.7 at your current usage level of 100K calls/month.
| Metric | Jamba Large 1.7 | Codestral 2508 | Winner |
|---|---|---|---|
| Overall Score | 40 | 40 | -- |
| Rank | #261 | #262 | Jamba Large 1.7 |
| Quality Rank | #261 | #262 | Jamba Large 1.7 |
| Adoption Rank | #261 | #262 | Jamba Large 1.7 |
| Parameters | -- | -- | -- |
| Context Window | 256K | 256K | -- |
| Pricing | $2.00/$8.00/M | $0.30/$0.90/M | -- |
| Signal Scores | |||
| Capabilities | 50 | 50 | Jamba Large 1.7 |
| Pricing | 92 | 99 | Codestral 2508 |
| Context window size | 86 | 86 | Jamba Large 1.7 |
| Recency | 83 | 82 | Jamba Large 1.7 |
| Output Capacity | 60 | 20 | Jamba Large 1.7 |
Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.
Scores 40/100 (rank #261), placing it in the top 10% of all 290 models tracked.
Scores 40/100 (rank #262), placing it in the top 10% of all 290 models tracked.
With only a 0-point gap, these models are in the same performance tier. The practical difference in output quality is minimal - your choice should depend on pricing, latency requirements, and specific feature needs.
Codestral 2508 offers 88% better value per quality point. At 1M tokens/day, you'd spend $18.00/month with Codestral 2508 vs $150.00/month with Jamba Large 1.7 - a $132.00 monthly difference.
Both models have comparable response speeds. For most applications, the latency difference is negligible.
When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.
Code generation & review
Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring
Customer support chatbot
Suitable for user-facing chat with competitive response times. Codestral 2508 also offers lower per-token costs for high-volume support
Long document analysis
Larger context window (256K tokens) can process longer documents, contracts, and research papers in a single pass
Batch data extraction
Lower output pricing ($0.90/M) reduces costs when processing thousands of records daily
Creative writing & content
Higher overall composite score (40/100) correlates with better nuance, coherence, and style in long-form content
Jamba Large 1.7 and Codestral 2508 are extremely close in overall performance (only 0 points apart). Your best choice depends entirely on which specific strengths matter most for your use case.
Best for Quality
Jamba Large 1.7
Marginally better benchmark scores; both are excellent
Best for Cost
Codestral 2508
88% lower pricing; better value at scale
Best for Reliability
Jamba Large 1.7
Higher uptime and faster response speeds
Best for Prototyping
Jamba Large 1.7
Stronger community support and better developer experience
Best for Production
Jamba Large 1.7
Wider enterprise adoption and proven at scale
by AI21 Labs
| Capability | Jamba Large 1.7 | Codestral 2508 |
|---|---|---|
| Vision (Image Input) | ||
| Function Calling | ||
| Streaming | ||
| JSON Mode | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
AI21 Labs
Mistral AI
Codestral 2508 saves you $11.58/month
That's 88% cheaper than Jamba Large 1.7 at 1,000 tokens/request and 100 requests/day.
Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.
| Parameter | Jamba Large 1.7 | Codestral 2508 |
|---|---|---|
| Context Window | 256K | 256K |
| Max Output Tokens | 4,096 | -- |
| Open Source | Yes | No |
| Created | Aug 8, 2025 | Aug 1, 2025 |