ALLaM 34B vs Jamba Large 1.7
| Signal | ALLaM 34B | Delta | Jamba Large 1.7 |
|---|---|---|---|
Capabilities | 17 | -33 | |
Pricing | 100 | +8 | |
Context window size | 57 | -28 | |
Recency | 69 | +3 | |
Output Capacity | 60 | -- | |
| Overall Result | 2 wins | of 5 | 2 wins |
Score History
40
current score
Tied
right now
40
current score
ALLaM 34B
HUMAIN
Jamba Large 1.7
AI21 Labs
ALLaM 34B saves you $600.00/month
That's $7200.00/year compared to Jamba Large 1.7 at your current usage level of 100K calls/month.
| Metric | ALLaM 34B | Jamba Large 1.7 | Winner |
|---|---|---|---|
| Overall Score | 40 | 40 | -- |
| Rank | #321 | #323 | ALLaM 34B |
| Quality Rank | #321 | #323 | ALLaM 34B |
| Adoption Rank | #321 | #323 | ALLaM 34B |
| Parameters | 34B | -- | -- |
| Context Window | 4K | 256K | Jamba Large 1.7 |
| Pricing | Free | $2.00/$8.00/M | -- |
| Signal Scores | |||
| Capabilities | 17 | 50 | Jamba Large 1.7 |
| Pricing | 100 | 92 | ALLaM 34B |
| Context window size | 57 | 86 | Jamba Large 1.7 |
| Recency | 69 | 66 | ALLaM 34B |
| Output Capacity | 60 | 60 | ALLaM 34B |
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 #321), placing it in the top -10% of all 290 models tracked.
Scores 40/100 (rank #323), placing it in the top -11% 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.
Choose ALLaM 34B when you need:
- High-volume production workloads where API costs must be minimized
Choose Jamba Large 1.7 when you need:
- Processing long documents or large codebases (256K token context)
- Agentic applications using tool/function calling
- Self-hosted deployments where you need full control over the model
Compare the cost per quality point to find the best value for your specific workload.
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. ALLaM 34B 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 ($0.00/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
ALLaM 34B and Jamba Large 1.7 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.
By Use Case
Best for Quality
ALLaM 34B
Marginally better benchmark scores; both are excellent
Best for Cost
ALLaM 34B
100% lower pricing; better value at scale
Best for Reliability
ALLaM 34B
Higher uptime and faster response speeds
Best for Prototyping
ALLaM 34B
Stronger community support and better developer experience
Best for Production
ALLaM 34B
Wider enterprise adoption and proven at scale
by HUMAIN
- Choose for Quality - Marginally better benchmark scores; both are excellent
- Choose for Cost - 100% lower pricing; better value at scale
- Choose for Reliability - Higher uptime and faster response speeds
- Choose for Prototyping - Stronger community support and better developer experience
- Choose for Production - Wider enterprise adoption and proven at scale
| Capability | ALLaM 34B | Jamba Large 1.7 |
|---|---|---|
| Vision (Image Input) | ||
| Function Callingdiffers | ||
| Streaming | ||
| JSON Modediffers | ||
| Reasoning | ||
| Web Search | ||
| Image Output |
ALLaM 34B
HUMAIN
Jamba Large 1.7
AI21 Labs
ALLaM 34B saves you $13.20/month
That's 100% 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 | ALLaM 34B | Jamba Large 1.7 |
|---|---|---|
| Context Window | 4K | 256K |
| Max Output Tokens | 4,096 | 4,096 |
| Open Source | No | Yes |
| Created | Aug 25, 2025 | Aug 8, 2025 |