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GPT-3.5 Turbo 16k vs Codestral 2508

GPT-3.5 Turbo 16k

OpenAI

40#361
vs
Codestral 2508

Mistral AI

40#324
Signal-by-Signal Comparison
SignalGPT-3.5 Turbo 16kDeltaCodestral 2508
Capabilities
50
--
50
Pricing
96
-3
99
Context window size
67
-19
86
Recency
0
-65
65
Output Capacity
60
+40
20
Overall Result
1 wins
of 5
3 wins
Codestral 2508 wins 3 of 5 signals

Score History

Score History (25 data points)
GPT-3.5 Turbo 16kCodestral 2508
GPT-3.5 Turbo 16k

40

current score

Leader

Tied

right now

Codestral 2508

40

current score

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

GPT-3.5 Turbo 16k

OpenAI

Per request$0.005000
Daily$16.67
Monthly$500.00
Annual$6000.00

Codestral 2508

Mistral AI

Best Value
Per request$0.000750
Daily$2.50
Monthly$75.00
Annual$900.00

Codestral 2508 saves you $425.00/month

That's $5100.00/year compared to GPT-3.5 Turbo 16k at your current usage level of 100K calls/month.

85% cheaper
Choose Codestral 2508 for cost optimization

GPT-3.5 Turbo 16k pricing:
Input:$3.00/M tokens
Output:$4.00/M tokens
Codestral 2508 pricing:
Input:$0.30/M tokens
Output:$0.90/M tokens
Tie
GPT-3.5 Turbo 16k

OpenAI

40

Composite Score

Tie
Codestral 2508

Mistral AI

40

Composite Score

Signal-by-Signal Comparison
MetricGPT-3.5 Turbo 16kCodestral 2508Winner
Overall Score
40
40
--
Rank#361#324
Codestral 2508
Quality Rank#361#324
Codestral 2508
Adoption Rank#361#324
Codestral 2508
Parameters------
Context Window16K256K
Codestral 2508
Pricing$3.00/$4.00/M$0.30/$0.90/M--
Signal Scores
Capabilities
50
50
GPT-3.5 Turbo 16k
Pricing
96
99
Codestral 2508
Context window size
67
86
Codestral 2508
Recency
0
65
Codestral 2508
Output Capacity
60
20
GPT-3.5 Turbo 16k
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.

GPT-3.5 Turbo 16kEntry Level

Scores 40/100 (rank #361), placing it in the top -24% of all 290 models tracked.

Raw Quality0/100
Cost Efficiency0/100
Speed0/100
Codestral 2508Entry Level

Scores 40/100 (rank #324), placing it in the top -11% 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 GPT-3.5 Turbo 16k when you need:

  • Budget-friendly applications with moderate quality requirements

Choose Codestral 2508 when you need:

  • High-volume production workloads where API costs must be minimized
  • Processing long documents or large codebases (256K token context)
Cost-Performance Analysis
GPT-3.5 Turbo 16k
Input cost$3.00/M tokens
Output cost$4.00/M tokens
Cost per quality point$0.175
Est. monthly (1M tokens/day)$105.00
Codestral 2508Best Value
Input cost$0.30/M tokens
Output cost$0.90/M tokens
Cost per quality point$0.030
Est. monthly (1M tokens/day)$18.00

Codestral 2508 offers 83% better value per quality point. At 1M tokens/day, you'd spend $18.00/month with Codestral 2508 vs $105.00/month with GPT-3.5 Turbo 16k - a $87.00 monthly difference.

Latency & Speed
GPT-3.5 Turbo 16kFaster
Speed score0/100
Codestral 2508
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

GPT-3.5 Turbo 16k

Customer support chatbot

Suitable for user-facing chat with competitive response times. Codestral 2508 also offers lower per-token costs for high-volume support

GPT-3.5 Turbo 16k

Long document analysis

Larger context window (256K tokens) can process longer documents, contracts, and research papers in a single pass

Codestral 2508

Batch data extraction

Lower output pricing ($0.90/M) reduces costs when processing thousands of records daily

Codestral 2508

Creative writing & content

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

GPT-3.5 Turbo 16k
Which Should You Choose?
Our recommendation:
GPT-3.5 Turbo 16k

GPT-3.5 Turbo 16k 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.

by OpenAI

  • Choose for Quality - Marginally better benchmark scores; both are excellent
  • 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 Mistral AI

  • Choose for Cost - 83% lower pricing; better value at scale
Capability Comparison
CapabilityGPT-3.5 Turbo 16kCodestral 2508
Vision (Image Input)
Function Calling
Streaming
JSON Mode
Reasoning
Web Search
Image Output
Monthly Cost Calculator
1,000tokens (600 in / 400 out)
100requests/day (3,000/month)

GPT-3.5 Turbo 16k

OpenAI

$10.20
estimated monthly cost

Codestral 2508

Mistral AI

Best Value
$1.62
estimated monthly cost

Codestral 2508 saves you $8.58/month

That's 84% cheaper than GPT-3.5 Turbo 16k 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
ParameterGPT-3.5 Turbo 16kCodestral 2508
Context Window16K256K
Max Output Tokens4,096--
Open SourceNoNo
CreatedAug 28, 2023Aug 1, 2025
Last updated: 28m ago

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GPT-3.5 Turbo 16k vs Codestral 2508 (2026) | LM Market Cap