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ALLaM 34B vs Gemini 3.7 Flash (batch)

ALLaM 34B

HUMAIN

40#342
vs
Signal-by-Signal Comparison
SignalALLaM 34BDeltaGemini 3.7 Flash (batch)
Capabilities
17
-83
100
Pricing
100
+1
99
Context window size
57
-38
96
Recency
67
-33
100
Output Capacity
60
-20
80
Overall Result
1 wins
of 5
4 wins
Gemini 3.7 Flash (batch) wins 4 of 5 signals

Score History

Score History (20 data points)
ALLaM 34BGemini 3.7 Flash (batch)
ALLaM 34B

40

current score

Leader

Tied

right now

Gemini 3.7 Flash (batch)

40

current score

LMMarketCap.com
Interactive Price Comparison
100Kcalls/month
1,000tokens (~1,333 chars)
500tokens (~667 chars)

ALLaM 34B

HUMAIN

Best Value
Per request$0.000000
Daily$0.00
Monthly$0.00
Annual$0.00

Gemini 3.7 Flash (batch)

Google

Per request$0.000656
Daily$2.19
Monthly$65.63
Annual$787.50

ALLaM 34B saves you $65.63/month

That's $787.50/year compared to Gemini 3.7 Flash (batch) at your current usage level of 100K calls/month.

100% cheaper
Choose ALLaM 34B for cost optimization

ALLaM 34B pricing:
Input:$0.00/M tokens
Output:$0.00/M tokens
Gemini 3.7 Flash (batch) pricing:
Input:$0.19/M tokens
Output:$0.94/M tokens
Tie
ALLaM 34B

HUMAIN

40

Composite Score

Tie
Gemini 3.7 Flash (batch)

Google

40

Composite Score

Signal-by-Signal Comparison
MetricALLaM 34BGemini 3.7 Flash (batch)Winner
Overall Score
40
40
--
Rank#342#243
Gemini 3.7 Flash (batch)
Quality Rank#342#243
Gemini 3.7 Flash (batch)
Adoption Rank#342#243
Gemini 3.7 Flash (batch)
Parameters34B----
Context Window4K1049K
Gemini 3.7 Flash (batch)
PricingFree$0.19/$0.94/M--
Signal Scores
Capabilities
17
100
Gemini 3.7 Flash (batch)
Pricing
100
99
ALLaM 34B
Context window size
57
96
Gemini 3.7 Flash (batch)
Recency
67
100
Gemini 3.7 Flash (batch)
Output Capacity
60
80
Gemini 3.7 Flash (batch)
Benchmark Interpretation

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.

ALLaM 34BEntry Level

Scores 40/100 (rank #342), placing it in the top -18% of all 290 models tracked.

Raw Quality0/100
Cost Efficiency0/100
Speed0/100
Gemini 3.7 Flash (batch)Entry Level

Scores 40/100 (rank #243), placing it in the top 17% of all 290 models tracked.

Raw Quality0/100
Cost Efficiency0/100
Speed0/100

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.

When to Use Each Model

Choose ALLaM 34B when you need:

  • High-volume production workloads where API costs must be minimized

Choose Gemini 3.7 Flash (batch) when you need:

  • Processing long documents or large codebases (1049K token context)
  • Multimodal workflows that require image understanding
  • Agentic applications using tool/function calling
  • Step-by-step reasoning and chain-of-thought problem solving
Cost-Performance Analysis
ALLaM 34B
Input cost$0.00/M tokens
Output cost$0.00/M tokens
Cost per quality point$0.000
Est. monthly (1M tokens/day)$0.00
Gemini 3.7 Flash (batch)
Input cost$0.19/M tokens
Output cost$0.94/M tokens
Cost per quality point$0.028
Est. monthly (1M tokens/day)$16.88

Compare the cost per quality point to find the best value for your specific workload.

Latency & Speed
ALLaM 34BFaster
Speed score0/100
Gemini 3.7 Flash (batch)
Speed score0/100

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.

Example Use Cases

Code generation & review

Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring

ALLaM 34B

Customer support chatbot

Suitable for user-facing chat with competitive response times. ALLaM 34B also offers lower per-token costs for high-volume support

ALLaM 34B

Long document analysis

Larger context window (1049K tokens) can process longer documents, contracts, and research papers in a single pass

Gemini 3.7 Flash (batch)

Batch data extraction

Lower output pricing ($0.00/M) reduces costs when processing thousands of records daily

ALLaM 34B

Creative writing & content

Higher overall composite score (40/100) correlates with better nuance, coherence, and style in long-form content

ALLaM 34B

Image understanding & OCR

Supports vision input - can analyze screenshots, diagrams, photos, and scanned documents directly

Gemini 3.7 Flash (batch)
Which Should You Choose?
Our recommendation:
ALLaM 34B

ALLaM 34B and Gemini 3.7 Flash (batch) 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.

ALLaM 34B
Recommended

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

by Google

Consider for specialized use cases.

Capability Comparison
CapabilityALLaM 34BGemini 3.7 Flash (batch)
Vision (Image Input)differs
Function Callingdiffers
Streaming
JSON Modediffers
Reasoningdiffers
Web Searchdiffers
Image Output
Monthly Cost Calculator
1,000tokens (600 in / 400 out)
100requests/day (3,000/month)

ALLaM 34B

HUMAIN

Best Value
$0.000000
estimated monthly cost

Gemini 3.7 Flash (batch)

Google

$1.46
estimated monthly cost

ALLaM 34B saves you $1.46/month

That's 100% cheaper than Gemini 3.7 Flash (batch) 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.

Parameters & Context
ParameterALLaM 34BGemini 3.7 Flash (batch)
Context Window4K1.0M
Max Output Tokens4,09665,536
Open SourceNoNo
CreatedAug 25, 2025Aug 13, 2026
Last updated: 9m ago

相关对比

ALLaM 34B vs Gemini 3.7 Flash (batch) (2026) | LM Market Cap